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.
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40 Own result · 26 Own interpretation · 9 Own method · 7 Not checked yet · 7 No basis given · 5 Sources not found · 1 Supported
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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)
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“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
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“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
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“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
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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
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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
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“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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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.
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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.
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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
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- 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
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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.
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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
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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
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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
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Caffe: Convolutional Architecture for Fast Feature Embedding
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The multimodal brain tumor image segmentation benchmark (BRATS)
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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
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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
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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.
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