The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index; 1 of the 15 sources its claims cite were checked for support.
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Open accessAbstract
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine tuning. We applied this framework to two applications: 2-D segmentation of multiple organs from fetal magnetic resonance (MR) slices, where only two types of these organs were annotated for training and 3-D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only the tumor core in one MR sequence was annotated for training. Experimental results show that: 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
40 Own result · 26 Own interpretation · 9 Own method · 7 Not checked yet · 7 No basis given · 5 Sources not found · 1 Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Deep learning with convolutional neural networks (CNNs) has achieved state-of-the-art performance for automated medical image segmentation [1].”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Introduction
What the sources say
- Supportedcites [1] Litjens G Kooi T Bejnordi BE Setio AAA Ciompi F Ghafoorian M van der Laak JA Ginneken BV Sánchez CI A survey on deep learning in medical image analysis Medical Image Analysis 2017 42 60 88 28778026 source
The survey reports excellent segmentation results from neural networks (Section 3.3.1, p. 12). It also reports that CNN-based systems were the highest-performing in the EM 2012 neuronal segmentation challenge and the highest-ranked in the GLAS gland segmentation challenge (Section 4.4, p. 19), supporting the state-of-the-art characterization. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy;”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs;”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It shows that BIFSeg achieves a larger improvement of accuracy from the initial segmentation when compared with the use of CRF or BIFSeg(-w).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“BIFSeg achieves significantly better accuracy (p-value < 0.05) for the placenta, and previously unseen fetal lungs and maternal kidneys compared with P-Net + CRF and BIFSeg(-w).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the placenta and fetal brain, FCN, U-Net and P-Net achieve visually similar results that are close to the ground truth.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After image-specific fine-tuning, most pixels in the outputs of BIFSeg(-w) and BIFSeg have a probability close to 0.0 or 1.0.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be observed that CRF and BIFSeg(-w) correct the initial segmentation moderately.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, for fetal lungs and maternal kidneys that are previously unseen in the training set, FCN and U-Net lead to a large region of under-segmentation.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since the central region of the tumor has a low intensity that is similar to the background, 3D GrabCut obtains large under-segmentations.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 39
- Probably not a substantial claim. 13
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, BIFSeg achieves better results with the same set of scribbles.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It shows that the performance of DeepMedic is low for T1c but high for FLAIR, and that of HighRes3DNet is the opposite.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average machine time for 3D GrabCut, DeepMedic, and PC-Net is 3.87s, 65.31s and 3.83s, respectively (on the laptop), and that for HighRes3DNet is 1.10s (on the cluster).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CRF improves the segmentation to some degree, but large areas of under-segmentation still exist.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It shows that BIFSeg takes noticeably less user time with similar or higher accuracy compared with the other three interactive segmentation methods.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be observed that GrabCut achieves a poor segmentation except for the fetal brain where there is a good contrast between the target and the background.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this 2D case, BIFSeg takes 0.72s in average for unsupervised image-specific fine-tuning.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“HighRes3DNet and PC-Net obtain similar results, but PC-Net is less complex and has lower memory consumption.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It shows that P-Net achieves the best accuracy for unseen fetal lungs and maternal kidneys with average machine time 0.16s.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and the CNNs outperform 3D GrabCut, with DeepMedic and PC-Net performing better than HighRes3DNet.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, BIFSeg performs better than CRF and BIFSeg(-w).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“BIFSeg improves the average Dice score from 82.66% to 86.13% for the tumor core, and from 83.52% to 86.29% for the whole tumor.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This acts as a post-processing step after the initial segmentation and out-performs CRF.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Since the scribbles are provided only in mis-segmented areas, the variations of position and length of scribbles are limited and much smaller than that of freely drawn scribbles used in traditional methods such as Random Walks [18] and Slic-Seg [21], and the output of BIFSeg also varies slightly with varying scribbles, as shown in Fig. 8.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
What the sources say
- Not checked yetcites [18] Grady L Schiwietz T Aharon S Westermann R Random walks for interactive organ segmentation in two and three dimensions: implementation and validation MICCAI 2005 773 780 16686030 source
- Not checked yetcites [21] Wang G Zuluaga MA Pratt R Aertsen M Doel T Klusmann M David AL Deprest J Vercauteren T Ourselin S Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views Medical Image Analysis 2016 34 137 147 27179367 PMC5052128 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, BIFSeg achieves better refined results for both the tumor core in T1c and the whole tumor in FLAIR.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DeepMedic takes over 60 seconds for inference, while HighRes3DNet has too large a memory consumption to work on a laptop.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experiments on segmenting multiple organs from 2D fetal MRI and brain tumors from 3D MRI show that our method performs well on previously unseen objects and the image-specific fine-tuning outperforms CRF.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“BIFSeg achieves similar or higher accuracy with fewer user interactions and less user time than traditional interactive segmentation methods.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our framework is general, flexible and can handle both 2D and 3D segmentations with few assumptions of network structures.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that the energy optimization problem of Eq. (1) remains well-posed with Eq. (9), (10), and (11).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method > Weighted Loss Function during Network Update Step
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, user-provided scribbles have much higher confidence than the other pixels, and they should have a higher impact on the loss function, leading to a weighted version of Eq. (3):”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method > Weighted Loss Function during Network Update Step
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“HighRes3DNet [8] works on 3D whole images with relatively fast inference but needs a large amount of GPU memory, leading to high hardware requirements.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Introduction
What the sources say
- Not matchedcites [8] Li W Wang G Fidon L Ourselin S Cardoso MJ Vercauteren T On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task IPMI 2017 348 360
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“For example, DeepMedic [7] works on 3D local patches to reduce memory requirements but results in a slow inference.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Introduction
What the sources say
- Not checked yetcites [7] Kamnitsas K Ledig C Newcombe VFJ Simpson JP Kane AD Menon DK Rueckert D Glocker B Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Medical Image Analysis 2017 36 61 78 27865153
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is based on the fact that our CNNs are designed and trained to learn some common features, such as saliency, contrast and hyper-intensity, across different objects, which helps to generalize to unseen objects.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- Rests on an assumption: Current CNNs do not generalize well to previously unseen object classes that are absent from the training set.
In the paper
“DeepIGeoS [5] combines CNNs with user interactions and has demonstrated good interactivity.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Introduction
What the sources say
- Not checked yetcites [5] Wang G Zuluaga MA Li W Pratt R Patel PA Aertsen M Doel T Klusmann M David AL Deprest J Ourselin S DeepIGeoS: A deep interactive geodesic framework for medical image segmentation arXiv preprint arXiv:1707.00652 2017 PMC6594450 29993532
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“It has been shown that image-specific adaptation of a pre-trained Gaussian Mixture Model (GMM) helps to improve segmentation accuracy [6].”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Introduction
What the sources say
- Not matchedcites [6] Ribeiro HL Gonzaga A Hand image segmentation in video sequence by GMM: a comprarative analysis SIBGRAPI 2006 357 364
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Here the tumor core in T1c images was previously seen while the whole tumor in FLAIR images was previously unseen for the CNNs.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is because DeepMedic has a small receptive field and tends to rely on local features. It is difficult to use local features to deal with T1c due to its complex appearance but easier to deal with FLAIR since the appearance is less complex.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Parameter setting was λ = 10.0, σ = 0.1, t0 = 0.2, t1 = 0.6, ϵ = 0.2, ω = 5.0 based on a grid search with the training data (i.e., whole tumor images were not used for parameter learning).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Here the fetal brain and the placenta were previously seen objects, and the fetal lungs and maternal kidneys were previously unseen objects.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plainn = 18- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We performed data splitting at patient level and used images from 10, 2, 6 patients for training, validation and testing, respectively.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plainn = 18- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“HighRes3DNet has a more complex model and tends to over-fit the tumor core.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 3D Segmentation of Brain Tumors from T1c and FLAIR
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Parameter setting was λ = 3.0, σ = 0.1, t0 = 0.2, t1 = 0.7, ϵ = 0.2, ω = 5.0 based on a grid search with the training data (i.e., fetal lungs and maternal kidneys were not used for parameter learning).”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Experiments and Results > 2D Segmentation of Multiple Organs from Fetal MRI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Table IV shows that DeepMedic achieves higher accuracy for the whole tumor. The reason is that our 3D experiment tends to learn to recognize hyper-intensity regions where local features have a higher influence than global features, and the relatively smaller receptive field of DeepMedic is more suitable for this task compared with the PC-Net and HighRes3DNet.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, these four organs were imaged with the same protocol. They have similar signal to noise ratio and share some common features, such as saliency, contrast and hyper-intensity.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Applying pre-trained models to previously unseen objects is a zero-shot learning problem [33].”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
What the sources say
- Not matchedcites [33] Zhang Z Saligrama V Zero-shot learning via semantic similarity embedding ICCV 2015 4166 4174
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It therefore reduces the efforts needed for gathering and annotating training data and can be applied to some unseen organs directly.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, the tumor core and whole tumor have some common features, e.g., lower or higher intensity than the remaining brain regions.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Though unsupervised fine-tuning helps to correct small mis-segmentations when the initial fully-automated performance is satisfactory, it may lead to under-performance when dealing with some complex cases, considering the distribution mismatch between the training and testing data.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Dealing with unseen objects is a major advantage compared with traditional CNNs and even transfer learning [25] or weakly supervised learning [3], since for some objects it does not require annotated instances for training at all.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
What the sources say
- Not checked yetcites [25] Tajbakhsh N Shin JY Gurudu SR Hurst RT Kendall CB Gotway MB Liang J Convolutional neural networks for medical image analysis: full training or fine tuning? TMI 2016 35 5 1299 1312 26978662 source
- Not checked yetcites [3] Rajchl M Lee M Oktay O Kamnitsas K Passerat-Palmbach J Bai W Rutherford M Hajnal J Kainz B Rueckert D DeepCut: Object segmentation from bounding box annotations using convolutional neural networks TMI 2017 36 2 674 683 PMC7115996 27845654 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure that PC-Net was suitable for the brain tumor segmentation task despite the gain in efficiency, we compared the initial fully automated output of PC-Net, DeepMedic and HighRes3D. Then, we only used PC-Net for the interactive segmentation pipeline of BIFSeg.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC-Net is more compact than HighRes3DNet and less likely to achieve over-fitting, leading to better ability to deal with the unseen whole tumor.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, compared with FCN and U-Net, P-Net has far fewer parameters without reduction of the receptive field. Therefore, it can generalize better to previously unseen objects.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our BIFSeg framework is theoretically applicable to different CNN models.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It uses a bounding box-based CNN for binary segmentation and can segment previously unseen objects.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In our experiments, hyper-parameters of BIFSeg (e.g., λ) were fixed globally in the testing stage.”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Discussion and Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Interactive segmentation remains the state of the art for existing commercial surgical planning and navigation products.
“As such, interactive segmentation remains the state of the art for existing commercial surgical planning and navigation products.”
The paper relies on this without support.
Current CNNs do not generalize well to previously unseen object classes that are absent from the training set.
“The first challenge of using CNNs for interactive segmentation is that current CNNs do not generalize well to previously unseen object classes that are not present in the training set.”
The paper relies on this without support.
Current CNNs are not adaptive to different test images because their parameters are learned from training images and then fixed during testing.
“but current CNNs are not adaptive to different test images, as parameters of the model are learned from training images and then fixed in the testing stage, without image-specific adaptation.”
The paper relies on this without support.
Interactive segmentation often requires image-specific learning to handle large context variations among different images.
“Second, interactive segmentation often requires image-specific learning to deal with large context variations among different images”
The paper relies on this without support.
Annotations for medical images are often expensive to acquire.
“For medical images, annotations are often expensive to acquire as both expertise and time are needed to produce accurate annotations.”
The paper relies on this without support.
Transitioning from GMMs to CNNs for image-specific adaptation has not yet been demonstrated.
“However, transitioning from simple GMMs to powerful but complex CNNs in this context has not yet been demonstrated.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“McClure al. [667] | et | 2018 | CL | Segmentation | Brain segmentation | 5 | DWC: Distributed weight consolidation”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [667]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while existing deep learning-based interactive segmentation methods focus mainly on limited tasks and image modalities.”
From Segment anything in medical images (2024) · cites this paper as [9]
Experimental — accuracy not yet measuredIn the citing paper
“We found that for all of these three networks, the segmentation accuracy of TTD remains close to that of the single-prediction baseline.”
From Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2019) · cites this paper as [4]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method > Unsupervised and Supervised Image-specific Fine-tuning
“(6)ϕ′(y^i|X^,θ)={+∞ifi∈Sandy^i=si0ifi∈Sandy^i≠si−logP(y^i|X^,θ)otherwise”
§ Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning > Method > Unsupervised and Supervised Image-specific Fine-tuning
“(4)argminY^{E(θ)=∑iϕ(y^i|X^,θ)+λ∑i,jψ(y^i,y^j|X^)}subjectto:y^i=siifi∈S”
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Research works printed but not linked: 2
- sermanet, 2014, Overfeat Integrated Recognition Localization and Detection Using Convolutional Networks
- vezhnevets, 2005, GrowCut: Interactive multi-label ND image segmentation by cellular automata, Proc GraphiCon, 150
Cited by
Sorted by citations
Items 201–300 of 842
Iglesias, J. E., Marc Modat, A., Peter, L., Stevens, A. A., Annunziata, R., Vercauteren, T. K., Lein, E. S., Fischl, B. R., & Sebastien Ourselin, A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.09.002 · Cited by 31
Self-citationDevoto, L., Muscroft, S., & Chand, M. (2019) · JAMA Surgery · doi:10.1001/jamasurg.2019.2546 · Cited by 32
Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare
Kumar, E. S., & Jayadev, P. S. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_5 · Cited by 32
An unsupervised 2D–3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation
Zhang, Y. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abe9f6 · Cited by 32
Kim, T.-S., Kim, J., Choi, H. S., Kim, E. S., Keum, B., Jeen, Y. T., Lee, H. S., Chun, H. J., Han, S. Y., Kim, D. U., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87737-3 · Cited by 35
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
Dorent, R., Joutard, S., Shapey, J., Kujawa, A., Marc Modat, A., Sebastien Ourselin, A., & Vercauteren, T. K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_57 · Cited by 32
Self-citationBonmati, E., Hu, Y., Sindhwani, N., Dietz, H., D'hooge, J., Barratt, D. C., Deprest, J. A., & Vercauteren, T. K. (2018) · Lirias (KU Leuven) · doi:10.1117/1.jmi · Cited by 32
Self-citationFastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Shen, Y., Li, J., Shao, X., Íñigo, B., Jindal, A., Dreizin, D., & Unberath, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_51 · Cited by 31
Artificial intelligence applied to fetal MRI: A scoping review of current research
Meshaka, R., Gaunt, T., & Shelmerdine, S. C. (2022) · British Journal of Radiology · doi:10.1259/bjr.20211205 · Cited by 33
TSE DeepLab: An efficient visual transformer for medical image segmentation
Yang, J., Tu, J., Zhang, X., Yu, S., & Xianyou, Z. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104376 · Cited by 31
A survey on deep learning models for detection of COVID-19
Mozaffari, J., Amirkhani, A., & Shokouhi, S. B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08683-x · Cited by 31
Sadr, S., Rokhshad, R., Daghighi, Y., Golkar, M., Kheybari, F. T., Gorjinejad, F., Kojori, A. M., Rahimirad, P., Shobeiri, P., Mahdian, M., et al. (2023) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twad001 · Cited by 34
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning
Leng, T., Zhang, Y., Han, K., & Xie, X. S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00774 · Cited by 33
A systematic review on deep learning based methods for cervical cell image analysis
Fang, M., Liao, B., Lei, X., & Wu, F. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128630 · Cited by 41
Souza, L. F. D. F., Silva, I. C. L., Marques, A. G., Silva, F. H. S., Nunes, V. X., Hassan, M. M., de Albuquerque, V. H. C., & Filho, P. P. R. (2020) · Sensors · doi:10.3390/s20236711 · Cited by 30
Trimpl, M. J., Primakov, S. P., Lambin, P., Stride, E., Vallis, K. A., & Gooding, M. J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac6d9c · Cited by 31
Udupa, J. K., Liu, T., Jin, C., Zhao, L., Odhner, D., Tong, Y., Agrawal, V., Pednekar, G. V., Nag, S., Kotia, T., et al. (2022) · Medical Physics · doi:10.1002/mp.15854 · Cited by 29
Han, Q., Wang, H., Hou, M., Weng, T., Pei, Y., Li, Z., Chen, G., Tian, Y., & Qiu, Z. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106343 · Cited by 30
Wang, J., Tian, S., Yu, L., Zhou, Z., Wang, F., & Wang, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107038 · Cited by 30
Hybrid attention for automatic segmentation of whole fetal head in prenatal ultrasound volumes
Yang, X., Wang, X., Wang, Y., Dou, H., Li, S., Wen, H., Yi, L., Heng, P., & Ni, D. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105519 · Cited by 30
Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net
Liu, Z., Su, J., Wang, R., Jiang, R., Song, Y., Zhang, D., Zhu, Y., Yuan, D., Gan, Q., & Sheng, V. S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116444 · Cited by 30
Multi-scale multi-hierarchy attention convolutional neural network for fetal brain extraction
Sun, L., Shao, W., Zhu, Q., Wang, M., Li, G., & Zhang, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109029 · Cited by 28
Yu, Y., & Yao, M. (2023) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2023.01.002 · Cited by 28
An overview on Meta-learning approaches for Few-shot Weakly-supervised Segmentation
Gama, P. H. T., Oliveira, H., dos Santos, J. A., & Junior, R. M. C. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.05.009 · Cited by 30
Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease
Senan, E. M., Alzahrani, A., Alzahrani, M. Y., Alsharif, N. H., & Aldhyani, T. H. H. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/6919483 · Cited by 27
Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning
Tran, T. V., Nguyen‐Xuan, H., & Zhuang, X. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1040-z · Cited by 26
Wang, W., Chen, Q., Iwamoto, Y., Aonpong, P., Lin, L., Hu, H., Zhang, Q., & Chen, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3011145 · Cited by 27
Lou, J., Xu, J., Zhang, Y., Sun, Y., Fang, A., Liu, J., Mur, L. A. J., & Ji, B. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107095 · Cited by 27
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Diaz‐Pinto, A., Mehta, P., Alle, S., Asad, M., Brown, R., Nath, V., Ihsani, A., Antonelli, M., Palkovics, D., Pintér, C., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17027-0_2 · Cited by 27
Self-citation3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images
Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3240238 · Cited by 27
Dual-stream Representation Fusion Learning for accurate medical image segmentation
Xu, R., Wang, C., Xu, S., Meng, W., & Zhang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106402 · Cited by 28
GAN inversion-based semi-supervised learning for medical image segmentation
Feng, X., Lin, J.-Y., Feng, C.-M., & Lu, G.-M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105536 · Cited by 25
COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
Mathesul, S., Swain, D., Satapathy, S. K., Rambhad, A., Acharya, B., Gerogiannis, V. C., & Kanavos, A. (2023) · Algorithms · doi:10.3390/a16100494 · Cited by 27
Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN
Togo, R., Ogawa, T., & Haseyama, M. (2019) · IEEE Access · doi:10.1109/access.2019.2925863 · Cited by 29
Deep learning for liver tumour classification: enhanced loss function
Randhawa, S., Alsadoon, A., Prasad, P. W. C., Al-Dala’in, T., Dawoud, A., & Alrubaie, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09900-8 · Cited by 27
A Review on Transferability Estimation in Deep Transfer Learning
Xue, Y., Yang, R., Chen, X., Liu, W., Wang, Z., & Liu, X. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3445892 · Cited by 28
A survey on machine learning based brain retrieval algorithms in medical image analysis
Sharma, A. K., Nandal, A., Dhaka, A., & Dixit, R. (2020) · Health and Technology · doi:10.1007/s12553-020-00471-0 · Cited by 27
Said, Y. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110912 · Cited by 27
Deep learning in structural and functional lung image analysis
Astley, J., Wild, J. M., & Tahir, B. (2021) · British Journal of Radiology · doi:10.1259/bjr.20201107 · Cited by 29
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
Jahanifar, M., Tajeddin, N. Z., Koohbanani, N. A., & Rajpoot, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00081 · Cited by 25
Hyper-fusion network for semi-automatic segmentation of skin lesions
Bi, L., FULHAM, M. J., & Kim, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102334 · Cited by 26
STKD: Distilling Knowledge From Synchronous Teaching for Efficient Model Compression
Su, T., Zhang, J., Yu, Z., Wang, G., & Liu, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3164264 · Cited by 28
Self-citationA Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images
Mostafa, F. A., Elrefaei, L. A., Fouda, M. M., & Hossam, A. (2022) · Diagnostics · doi:10.3390/diagnostics12123034 · Cited by 26
Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy
Wei, Z., Ren, J., Korreman, S. S., & Nijkamp, J. A. (2022) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2022.12.005 · Cited by 25
Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation
Rettenberger, L., Schilling, M. P., Elser, S., Böhland, M., & Reischl, M. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3252889 · Cited by 26
Bonmati, E., Hu, Y., Sindhwani, N., Dietz, H. P., D’hooge, J., Barratt, D. C., Deprest, J. A., & Vercauteren, T. K. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.021206 · Cited by 26
Self-citationUncertainty-based graph convolutional networks for organ segmentation refinementPreprint
Soberanis-Mukul, R. D., Navab, N., & Albarqouni, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02191 · Cited by 26
Unfolding Explainable AI for Brain Tumor Segmentation
Hassan, M. A., Fateh, A. A., Lin, J., Zhuang, Y., Lin, G., Xiong, H., You, Z., Qin, P., & Zeng, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128058 · Cited by 28
Sheela, C. J. J., & Suganthi, G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09006-1 · Cited by 26
Tang, X., Peng, J., Zhong, B., Li, J., & Yan, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106110 · Cited by 25
Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification
Rajaragavi, R., & Rajan, S. P. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.021206 · Cited by 25
Tomar, D., Tomar, P., Bhardwaj, A., & Sinha, G. R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7216959 · Cited by 30
Explainable multi-module semantic guided attention based network for medical image segmentation
Karri, M., Annavarapu, C. S. R., & Acharya, U. R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106231 · Cited by 25
Automatic Fetal Brain Extraction Using Multi-stage U-Net with Deep Supervision
Lou, J., Li, D., Bui, T. D., Zhao, F., Sun, L., Li, G., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_68 · Cited by 26
Deep Learning With Radiogenomics Towards Personalized Management of Gliomas
Mitra, S. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3075500 · Cited by 26
Wang, H., Huai, L., Li, W., Qi, L., Jiang, X., & Shi, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3549433 · Cited by 25
Self-citationMHSU-Net: A more versatile neural network for medical image segmentation
Ma, H., Zou, Y., & Liu, P. X. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106230 · Cited by 24
Multi-label active learning through serial–parallel neural networks
Min, X.-Y., Qian, K., Zhang, B.-W., Song, G., & Min, F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109226 · Cited by 24
Kujawa, A., Dorent, R., Connor, S. E. J., Thomson, S., Ivory, M., Vahedi, A., Guilhem, E., Wijethilake, N., Bradford, R., Kitchen, N. D., et al. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1365727 · Cited by 24
Self-citationArtificial Intelligence in Obstetrics and Gynecology: Transforming Care and Outcomes
Patel, D. J., Chaudhari, K., Acharya, N., Shrivastava, D., & Muneeba, S. (2024) · Cureus · doi:10.7759/cureus.64725 · Cited by 35
Fu, X., Cai, N., Huang, K., Wang, H., Wang, P., Liu, C., & Wang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2946582 · Cited by 23
Skin lesion segmentation based on mask RCNN , Multi Atrous Full‐CNN , and a geodesic method
Bagheri, F., Tarokh, M. J., & Ziaratban, M. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22561 · Cited by 22
Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning
Shaziya, H., & Shyamala, K. (2020) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon50868.2020.9362463 · Cited by 23
Shape and boundary-aware multi-branch model for semi-supervised medical image segmentation
Liu, X., Hu, Y., Chen, J., & Li, K. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105252 · Cited by 23
A semi-supervised learning approach for COVID-19 detection from chest CT scans
Zhang, Y., Su, L., Liu, Z., Tan, W., Jiang, Y., & Cheng, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.076 · Cited by 24
Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
Benedetti, P., Femminella, M., & Reali, G. (2022) · Applied Sciences · doi:10.3390/app13010329 · Cited by 23
Zhu, J., Bolsterlee, B., Chow, B. V. Y., Song, Y., & Meijering, E. H. W. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102383 · Cited by 28
JellyNet: The convolutional neural network jellyfish bloom detector
Mcilwaine, B., & Casado, M. R. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102279 · Cited by 24
A deep learning approach to track Arabidopsis seedlings’ circumnutation from time-lapse videos
Mao, Y., Liu, H., Wang, Y., & Brenner, E. D. (2023) · Plant Methods · doi:10.1186/s13007-023-00984-5 · Cited by 22
Context aware machine learning techniques for brain tumor classification and detection – A review
Amjad, U., Raza, A., Fahad, M., Farid, D., Akhunzada, A., Abubakar, M., & Beenish, H. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e41835 · Cited by 24
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation
Ding, Y., Liu, J., Xu, X., Huang, M., Zhuang, J., Xiong, J., & Shi, Y. (2020) · Medical Imaging with Deep Learning · Cited by 21
Image forgery detection using Deep Neural Network
Singh, A., & Singh, J. (2021) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin52536.2021.9565953 · Cited by 22
Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG
He, Z., Tang, M., Wang, P., Du, L., Chen, X., Cheng, G., & Fang, Z. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104501 · Cited by 22
GSAL: Geometric structure adversarial learning for robust medical image segmentation
Wang, K., Zhang, X., Lü, Y., Zhang, W., Huang, S., & Yang, D. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109596 · Cited by 22
Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network
Jensen, C., Sørensen, K. S., Jørgensen, C. K., Nielsen, C. W., Høy, P. C., Langkilde, N. C., & Østergaard, L. R. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.014501 · Cited by 22
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
Zhou, T., Li, L., Bredell, G., Li, J., & Konukoğlu, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_52 · Cited by 24
AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural NetworksPreprint
Roy, S. K., Paoletti, M. E., Haut, J. M., Dubey, S. R., Kar, P., Plaza, A., & Chaudhuri, B. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.10190 · Cited by 31
Kim, T., On, S., Gwon, J. G., & Kim, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-59735-8 · Cited by 21
Liver segmentation and metastases detection in MR images using convolutional neural networks
Jansen, M. J. A., Kuijf, H. J., Niekel, M., Veldhuis, W. B., Wessels, F. J., Viergever, M. A., Pluim, J. P. W., Beeldverwerking, I. S. I., Cancer, Brain, et al. (2019) · TU/e Research Portal · Cited by 21
Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning
Xing, R., Niu, S., Gao, X., Liu, T., Fan, W., & Chen, Y. (2021) · Biomedical Optics Express · doi:10.1364/boe.416167 · Cited by 21
Mehrnia, S. S., Safahi, Z., Mousavi, A., Panahandeh, F., Farmani, A., Yuan, R., Rahmim, A., & Salmanpour, M. R. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01458-x · Cited by 20
Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC
Wang, Y., Qi, Q., & Shen, X. (2020) · Brain Sciences · doi:10.3390/brainsci10020116 · Cited by 20
Deep Learning Automatic Fetal Structures Segmentation in MRI Scans with Few Annotated Datasets
Dudovitch, G., Link‐Sourani, D., Ben‐Sira, L., Miller, E., Bashat, D. B., & Joskowicz, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_35 · Cited by 20
Meta-Learning Initializations for Interactive Medical Image Registration
Baum, Z. M. C., Hu, Y., & Barratt, D. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218147 · Cited by 22
A Breast Cancer Diagnosis Framework Based on Machine Learning
Akhtar, N., Pant, D. H., Dwivedi, A., Jain, V., & Perwej, Y. (2023) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset2310375 · Cited by 20
A survey on artificial intelligence in pulmonary imaging
Saha, P. K., Nadeem, S. A., & Comellas, A. P. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1510 · Cited by 23
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Chen, Y., Liu, L., Li, J., Jiang, H., Ding, C. H. Q., & Zhou, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_67 · Cited by 22
Segmentation of hard exudate lesions in color fundus image using two-stage CNN-based methods
Do, Q. V., Hoang, H. T., Vu, N., Jesus, D. A. D., Brea, L. S., Nguyen, H. X., Nguyen, A. T. L., Le, T. N., Dinh, D. T. M., Nguyen, M. T. B., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122742 · Cited by 21
Wen, L., Guo, E., Zhao, H. L., Li, Y., Miao, L., Liu, C., & Sun, W. (2024) · BMC Oral Health · doi:10.1186/s12903-024-04460-x · Cited by 20
Interactive prostate MR image segmentation based on ConvLSTMs and GGNN
Tian, Z., Li, X., Chen, Z., Zheng, Y., Fan, H., Li, Z., Li, C., & Du, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.05.121 · Cited by 20
Li, R., & Chen, X. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106534 · Cited by 20
Artificial intelligence for radiopharmaceutical and molecular imaging
Tao, J., Liang, L., Hao, S., Chen, Y., Yang, Z., Cai, Y., & Zhu, H. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.039 · Cited by 23
A Deep Learning-Based Workflow for Dendritic Spine Segmentation
Vidaurre-Gallart, I., Fernaud-Espinosa, I., Cosmin-Toader, N., Talavera-Martínez, L., Martín-Abadal, M., Benavides‐Piccione, R., González-Cid, Y., Pastor, L., DeFelipe, J., & García, M. (2022) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2022.817903 · Cited by 17
Noh, J., Nam, Y.-H., Lee, S., Lee, I.-G., Kim, Y., Lee, J., & Rho, J. (2022) · Photonics and Nanostructures - Fundamentals and Applications · doi:10.1016/j.photonics.2022.101071 · Cited by 19
Wang, Z., Zhao, D., Heidari, A. A., Chen, Y., Chen, H., & Liang, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-63739-9 · Cited by 19
Weakly Supervised Segmentation from Extreme Points
Roth, H. R., Zhang, L., Yang, D., Milletarì, F., Xu, Z., Wang, X., & Xu, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33642-4_5 · Cited by 19
Ciprián-Sánchez, J. F., Ochoa‐Ruiz, G., Rossi, L., & Morandini, F. (2021) · Applied Sciences · doi:10.3390/app11157046 · Cited by 19
Su, Y.-H., Jiang, W., Chitrakar, D., Huang, K., Peng, H., & Hannaford, B. (2021) · Sensors · doi:10.3390/s21155163 · Cited by 19
Deep Learning in Multi-organ SegmentationPreprint
Lei, Y., Fu, Y., Wang, T., Qiu, R. L. J., Curran, W. J., Liu, T., & Yang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10619 · Cited by 26
An improved segmentation algorithm of CT image based on U-Net network and attention mechanism
Yang, J., & Qiu, K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10841-z · Cited by 18
Metadata
- Authors
- Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., & Vercauteren, T.
- Affiliations
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences (GB)
- KU Leuven (BE)
- Corresponding author
- Wang, Guotai
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-07
- Volume
- 37
- Issue
- 7
- Pages
- 1562-1573
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC6051485
- OpenAlex
- W2763160469
- Cited by
- 843
- References
- 42
- Citations by year
- 2026: 37 · 2025: 82 · 2024: 102 · 2023: 112 · 2022: 132 · 2021: 146 · 2020: 130 · 2019: 76 · 2018: 26
- FWCI
- 47.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Wellcome Trust (WT101957, WT97914, HICF-T4-275)
- Engineering and Physical Sciences Research Council (NS/A000027/1, EP/H046410/1, EP/J020990/1, EP/K005278, NS/A000050/1, 203145Z/16/Z)
- Royal Society (RG160569)
- University College London
- Great Ormond Street Hospital Charity
- Nvidia
- Emerald, a GPU-accelerated High Performance Computer, made available by the Science and Engineering South Consortium operated in partnership with the STFC Rutherford-Appleton Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- zero-shot learning, scribble-based segmentation, weighted loss function, uncertainty estimation, brain tumor segmentation, magnetic resonance imaging, convolutional neural networks
- MeSH terms
- Deep Learning, Brain, Brain Neoplasms, Female, Fetus, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pregnancy, Prenatal Diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
