V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Open accessAbstract
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 7 cited sources checked · 0 supported · 0 flagged
- Findingown data
On the PROMISE 2012 test set, V-Net with a Dice-based loss achieved an average Hausdorff distance of 5.71 ± 1.20 mm.
“V-Net + Dice-based loss 0.869 ± 0.033 | 5.71 ± 1.20 mm | 82.39”
Quote in the paperSubstantial claim - Findingown data
On the PROMISE 2012 test set, V-Net with a Dice-based loss achieved a challenge score of 82.39.
“V-Net + Dice-based loss 0.869 ± 0.033 | 5.71 ± 1.20 mm | 82.39”
Quote in the paperSubstantial claim - Findingown data
On the PROMISE 2012 test set, V-Net with a multinomial logistic loss achieved a challenge score of 63.30.
“V-Net + mult. logistic loss 0.739 ± 0.088 | 10.55 ± 5.38 mm | 63.30”
Quote in the paperNot a substantial claim - Findingown data
The innermost portion of the CNN already captures the content of the whole input volume.
“We report in Table 1 the receptive fields of each network layer, showing the fact that the innermost portion of our CNN already captures the content of the whole input volume.”
Quote in the paperSubstantial claim - Findingown data
The Dice-based loss produced better results than the same network trained with a multinomial logistic loss and sample re-weighting.
“we obtain results that we experimentally observed are much better than the ones computed through the same network trained optimising a multinomial logistic loss with sample re-weighting”
Quote in the paperSubstantial claim
- Findingown data
The residual-learning architecture converges in a fraction of the time required by a similar network without residual functions.
“As confirmed by our empirical observations, this architecture ensures convergence in a fraction of the time required by a similar network that does not learn residual functions.”
Quote in the paperNot a substantial claim - Findingown data
The feature-forwarding connections improve the model's convergence time.
“We also observed that when these connections improve the convergence time of the model.”
Quote in the paperNot a substantial claim - Findingown data
On the PROMISE 2012 test set, V-Net with a multinomial logistic loss achieved an average Dice coefficient of 0.739 ± 0.088.
“V-Net + mult. logistic loss 0.739 ± 0.088 | 10.55 ± 5.38 mm | 63.30”
Quote in the paperSubstantial claim - Findingown data
On the PROMISE 2012 test set, V-Net with a Dice-based loss achieved an average Dice coefficient of 0.869 ± 0.033.
“V-Net + Dice-based loss 0.869 ± 0.033 | 5.71 ± 1.20 mm | 82.39”
Quote in the paperSubstantial claim - Findingown data
The trained network can segment a previously unseen MRI volume in about one second.
“we were able to segment a previously unseen volume in circa 1 second”
Quote in the paperNot a substantial claim - Findingreasoning
Forwarding features from early compression stages to the decompression path improves the quality of the final contour prediction.
“In this way we gather fine grained detail that would be otherwise lost in the compression path and we improve the quality of the final contour prediction.”
Quote in the paperSubstantial claim - Findingown data
On the PROMISE 2012 test set, V-Net with a multinomial logistic loss achieved an average Hausdorff distance of 10.55 ± 5.38 mm.
“V-Net + mult. logistic loss 0.739 ± 0.088 | 10.55 ± 5.38 mm | 63.30”
Quote in the paperNot a substantial claim - Findingown data
Data augmentation was necessary to obtain robustness and increased precision on the test dataset.
“In this work we found necessary to augment the original training dataset in order to obtain robustness and increased precision on the test dataset.”
Quote in the paperSubstantial claim- Rests on an assumption: Annotated medical volumes are difficult to obtain because experts must manually trace reliable ground truth and there is a cost to acquiring them.
- Interpretationreasoning
The Dice-based loss formulation does not require assigning class-specific weights to balance foreground and background voxels.
“Using this formulation we do not need to assign weights to samples of different classes to establish the right balance between foreground and background voxels”
Quote in the paperSubstantial claim - Interpretationreasoning
The approach can handle strong imbalance between the number of foreground and background voxels.
“In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels.”
Quote in the paperSubstantial claim - Premisecitation
Before this work, fully convolutional networks trained end-to-end had been applied only to 2D images in computer vision and microscopy.
“Fully convolutional network trained end-to-end were so far applied only to 2D images both in computer vision [11,8] and microscopy image analysis [14].”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisereasoning
The PROMISE 2012 dataset is representative of the clinical variability and challenges encountered in clinical settings.
“The data in this dataset is representative of the clinical variability and challenges encountered in clinical settings.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] source
Not checked in the free run.
- Premisecitation
The test set was representative of the clinical variability encountered in prostate scans in real clinical settings.
“The test set was representative of the clinical variability encountered in prostate scans in real clinical settings [7].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] source
Not checked in the free run.
- May combine two claims. 6
- Classified as probably not a substantial claim. 2
May combine two claims.
Findingown dataThe approach achieves good performance on challenging test data while needing only a fraction of the processing time required by other previous methods.
“We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataV-Net performs segmentation of MRI prostate volumes in a fast and accurate manner.
“We presented and approach based on a volumetric convolutional neural network that performs segmentation of MRI prostate volumes in a fast and accurate manner.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenThe CNN is trained end-to-end on prostate MRI volumes and predicts segmentation for the entire volume at once.
“Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe architecture, inspired by U-Net but divided into stages that learn residuals, improves both results and convergence time.
“Although we inspired our architecture to the one proposed in [14], we divided it into stages that learn residuals and, as empirically observed, improve both results and convergence time.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe results reported in the paper were obtained directly from the challenge organisers after the authors submitted their segmentations.
“All the results reported in this section of the paper were obtained directly from the organisers of the challenge after submitting the segmentation obtained through our approach.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationShallower layers capture local information while deeper layers capture global information.
“Shallower layers grasp local information while deeper layers use filters whose receptive fields are much broader that therefore capture global information [19].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19]
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe deepest layer's features perceive the whole anatomy at once and impose global constraints, which is important for segmenting poorly visible anatomy.
“We believe that this characteristic is important during segmentation of poorly visible anatomy: the features computed in the deepest layer perceive the whole anatomy of interest at once, since they are computed from data having a spatial support much larger than the typical size of the anatomy we seek to delineate, and therefore impose global constraints.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Annotated medical volumes are difficult to obtain because experts must manually trace reliable ground truth and there is a cost to acquiring them.
“Annotated medical volumes are not easy to obtain due to the fact that one or more experts are required to manually trace a reliable ground truth annotation and that there is a cost associated with their acquisition.”
The paper relies on this without support.
Patch-wise segmentation approaches are prone to failure because they use only local context, especially in challenging modalities such as ultrasound.
“Such segmentations are obtained by only considering local context and therefore are prone to failure, especially in challenging modalities such as ultrasound, where a high number of mis-classified voxel are to be expected.”
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?
Milletrate et al. implemented a 3D CNN for segmenting MRI prostate images, attaining a 0.869 Dice coefficient score, the same as the winning teams in the PROMISE2012 competition.
“Milletrate et al. [304] implemented 3D CNN for segmenting MRI prostate images. Furthermore, they used the PROMISE2012 challenge dataset, from which fifty MRI scans were used for training and thirty for testing. The U-Net architecture of Ronnerberger et al. [305] inspired their V-net. This model attained a 0.869 Dice coefficient score, the same as the winning teams in the competition.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [304]
The paper describes a volumetric CNN for prostate MRI segmentation and reports an average Dice coefficient of 0.869 for V-Net with Dice-based loss (Conclusion, Table 2). It also reports 50 PROMISE2012 training volumes and 30 test volumes. But Table 2 gives the leading comparison methods Dice coefficients of 0.879 and 0.874, not the same 0.869 score. (LLM, conf=0.88)
Experimental — accuracy not yet measuredV-net was suggested as an extension of U-net for segmentation of the prostate on volumetric MRI images.
“For example, V-net was suggested as an extension of U-net for segmentation of the prostate on volumetric MRI images [49].”
From Convolutional neural networks: an overview and application in radiology (2018) · cites this paper as [49]
V-net utilized a loss function based on the Dice coefficient between segmentation results and ground truth, which directly reflected the quality of prostate segmentation.
“In the study, V-net utilized a loss function based on the Dice coefficient between segmentation results and ground truth, which directly reflected the quality of prostate segmentation.”
From Convolutional neural networks: an overview and application in radiology (2018) · cites this paper as [49]
We compared our nerve segmentation model with other advanced 3D image segmentation networks, training each on the same training dataset until full convergence.
“We compared our nerve segmentation model with other advanced 3D image segmentation networks50 (Supplementary Table 1): VNet51, Attention U-Net52, nnFormer53, UNETR54, SwinUNETR55, nnU-Net56 and nnUNetRes56. Each network was trained on the same training dataset until full convergence, defined as no decrease in the training loss for ten consecutive epochs.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [51]
We implemented comparison baselines (3D UNet, V-Net, Attention U-Net, nnFormer, UNETR) trained with the nnU-Net pipeline, and selected the finetuned VesselFM based on voxel Dice and instance Dice metrics from fivefold cross validation.
“As comparison baselines, we implemented the following architectures: 3D UNet57, V-Net51, Attention U-Net52, nnFormer53 and UNETR54. All were trained by using the nnU-Net pipeline56, with a patch size of 128 × 128 × 128 voxels, channel-wise z-score normalization, learning rate decay and SGD optimizer. The baselines were trained until convergence for 1,000 epochs and initial learning rates of [0.000”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [51]
Deep learning based segmentation techniques surpassed traditional computer vision methods for dense semantic segmentation.
“Recently, deep learning based segmentation techniques surpassed traditional computer vision methods for dense semantic segmentation.”
From 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (2019) · cites this paper as [19]
Convolutional neural networks demonstrate state-of-the-art segmentation accuracy both in 2D natural images and in 3D medical image modalities.
“Convolutional neural networks (CNN) are able to learn from examples and demonstrate state-of-the-art segmentation accuracy both in 2D natural images [6] and in 3D medical image modalities [19].”
From 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (2019) · cites this paper as [19]
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.
Table 1 names a different convolution kernel size
MinorThe table caption describes 3 × 3 × 3 convolutional layers, while the methods section says the convolutions in each stage use 5×5×5 kernels.
“Table: 1 Table 1 . Theoretical receptive field of the 3 × 3 × 3 convolutional layers of the network.”
“The convolutions performed in each stage use volumetric kernels having size 5×5×5 voxels.”
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
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 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119 · see citing works
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,748 · see citing works
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 1× · see citing works
“Post-processing approaches such as connected components analysis normally yield no improvement and therefore, more recent works, propose to use the network predictions in combination with Markov random fields [6], voting strategies [9] or more traditional approaches such as level-sets [2].”
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works
Evaluation of prostate segmentation algorithms for mri: The promise12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works
Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis
Crum, W. R., Camara, O., & Hill, D. L. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.880587 · Cited by 776 · see citing works
Cha, K., Hadjiiski, L., Samala, R. K., Chan, H., Caoili, E., & Cohan, R. (2016) · Medical Physics (Lancaster) · doi:10.1118/1.4944498 · Cited by 248 · see citing works
Roehrborn, C. G., Boyle, P., Bergner, D., Gray, T., Gittelman, M., Shown, T., Melman, A., Bracken, R. B., deVere White, R., Taylor, A., et al. (1999) · Urology · doi:10.1016/s0090-4295(99)00232-0 · Cited by 233 · see citing works
Moradi, M., Abolmaesumi, P., Siemens, D. R., et al. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2009766 · Cited by 92 · see citing works
Multimodal image-guided prostate fusion biopsy based on automatic deformable registration
Zettinig, O., Shah, A. M., Hennersperger, C., Eiber, M. J., Kroll, C., Kübler, H. R., Maurer, T., Milletarì, F., Rackerseder, J., zu Berge, C. S., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1233-y · Cited by 77 · see citing works
Self-citationA qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer
Huyskens, D. P., Maingon, P., Vanuytsel, L., et al. (2008) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2008.08.007 · Cited by 65 · see citing works
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and UltrasoundPreprint
Milletarì, F., Ahmadi, S., Kroll, C., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07014 · Cited by 31 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- porter, 2003, Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer, Oncology, 17, 1395
- sermanet, 2013, Overfeat Integrated Recognition Localization and Detection Using Convolutional Networks
- cicek, 2016, 3D U- Net Learning Dense Volumetric Segmentation from Sparse Annotation
- bernard, 2015, Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography, Medical Imaging IEEE Transactions on
- vincent, 2012, Fully automatic segmentation of the prostate using active appearance models, MICCAI Grand Challenge PROMISE 2012
Cited by
Sorted by citations
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
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 · see citing works
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works
“We supervise mask prediction with the linear combination of focal loss [65] and dice loss [73] used in [14].”
MethodnnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477 · see citing works
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · see citing works
“For example, V-net was suggested as an extension of U-net for segmentation of the prostate on volumetric MRI images [49].”
BackgroundTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv · doi:10.48550/arxiv.2102.04306 · Cited by 4,206 · see citing works
“We conduct main experiments on Synapse multi-organ segmentation dataset by comparing our TransUNet with four previous state-of-the-arts: 1) V-Net [10]; 2) DARR [5]; 3) U-Net [13] and 4) AttnUNet [14].”
MethodImage segmentation using deep learning: A survey
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796 · see citing works
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00181 · Cited by 3,586 · see citing works
“Our loss function is a combination of soft dice loss [32] and cross-entropy loss, and it can be computed in a voxel-wise manner according to”
MethodMasked-attention Mask Transformer for Universal Image Segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00135 · Cited by 3,565 · see citing works
Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913 · see citing works
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014 · see citing works
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works
3D MRI Brain Tumor Segmentation Using Autoencoder Regularization
Myronenko, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11726-9_28 · Cited by 1,283 · see citing works
Medical image analysis using convolutional neural networks: a review
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1088-1 · Cited by 1,431 · see citing works
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095 · see citing works
“Milletari et al. (2016) also apply a dense deformation field through a 2x2x2 grid of control-points and B-spline interpolation on the training images.”
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891 · see citing works
Interactive medical image segmentation using deep learning with image-specific fine tuning
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843 · see citing works
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.103 · Cited by 659 · see citing works
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662 · see citing works
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579 · see citing works
Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works
NiftyNet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552 · see citing works
Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks
Roy, A. G., Navab, N., & Wachinger, C. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867261 · Cited by 534 · see citing works
Self-citationDeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
“A similar network (V-Net) [27] was proposed to segment the prostate from 3D MRI volumes.”
BackgroundNAS-Unet: Neural Architecture Search for Medical Image Segmentation
Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487 · see citing works
“[30] proposed a 3D deformation architecture called V-net based on U-Net.”
MethodA Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images
Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., & Zhang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3000314 · Cited by 469 · see citing works
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.605132 · Cited by 468 · see citing works
Confidence calibration and predictive uncertainty estimation for deep medical image segmentation
Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P., & Kapur, T. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3006437 · Cited by 428 · see citing works
The present and future of deep learning in radiology
Saba, L., Biswas, M., Kuppili, V., Cuadrado Godia, E., Suri, H. S., Edla, D. R., Omerzu, T., Laird, J. R., Khanna, N. N., Mavrogeni, S., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.038 · Cited by 353 · see citing works
Machine learning analysis of whole mouse brain vasculature
Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0792-1 · Cited by 338 · see citing works
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303 · see citing works
Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300 · see citing works
Semantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288 · see citing works
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. (2019) · IEEE Access · doi:10.1109/access.2019.2947606 · Cited by 253 · see citing works
Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894322 · Cited by 227 · see citing works
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226 · see citing works
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H., & Xu, D. (2021) · arXiv · doi:10.48550/arxiv.2103.10504 · Cited by 3,580 · see citing works
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188 · see citing works
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Wu, Y.-H., Liu, Y., Xu, J., Bian, J.-W., Gu, Y.-C., & Cheng, M.-M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3134684 · Cited by 176 · see citing works
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., & Roth, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093608 · Cited by 170 · see citing works
FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., & Lam, T. L. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636084 · Cited by 170 · see citing works
PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
Wang, W., Xie, E., Li, X., Liu, X., Liang, D., Zhibo, Y., Lu, T., & Shen, C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3077555 · Cited by 140 · see citing works
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
Dorent, R., Joutard, S., Modat, M., Ourselin, S., & Vercauteren, T. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_9 · Cited by 160 · see citing works
DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
Unberath, M., Zaech, J.-N., Lee, S. C., Bier, B., Fotouhi, J., Armand, M., & Navab, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00937-3_12 · Cited by 145 · see citing works
“63) % which is in line with results re-ported in previous studies using this architecture [3].”
BackgroundResultSelf-citationM3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133 · see citing works
ToolNet: Holistically-nested real-time segmentation of robotic surgical tools
Garcia-Peraza-Herrera, L. C., Li, W., Fidon, L., Gruijthuijsen, C., Devreker, A., Attilakos, G., Deprest, J., Poorten, E. V., Stoyanov, D., Vercauteren, T., et al. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206462 · Cited by 137 · see citing works
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
Deng, S., Xu, X., Wu, C., Chen, K., & Jia, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00182 · Cited by 124 · see citing works
Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117 · see citing works
Zeng, Y., Tsui, P.-H., Wu, W., Zhou, Z., & Wu, S. (2021) · Journal of Digital Imaging · doi:10.1007/s10278-020-00410-5 · Cited by 113 · see citing works
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2018) · JCI Insight · doi:10.1172/jci.insight.120178 · Cited by 111 · see citing works
Autofocus Layer for Semantic Segmentation
Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., & Nori, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00931-1_69 · Cited by 135 · see citing works
Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data
Hausen, R., & Robertson, B. E. (2020) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab8868 · Cited by 110 · see citing works
“A common approach to handle unbalanced segmentations is to employ a Dice loss function to supplement the entropy loss function (e.g., Milletari et al. 2016; Sudre et al. 2017).”
MethodMok, T. C. W., & Chung, A. C. S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_7 · Cited by 95 · see citing works
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
Ambrosini, P., Ruijters, D., Niessen, W. J., Moelker, A., & van Walsum, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66185-8_65 · Cited by 102 · see citing works
Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84 · see citing works
Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors
Graves, A. R., Roth, R. H., Tan, H. L., Zhu, Q., Bygrave, A. M., Lopez-Ortega, E., Hong, I., Spiegel, A. C., Johnson, R. C., Vogelstein, J. T., et al. (2021) · eLife · doi:10.7554/elife.66809 · Cited by 84 · see citing works
Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
Grupp, R. B., Unberath, M., Gao, C., Hegeman, R. A., Murphy, R. J., Alexander, C. P., Otake, Y., McArthur, B. A., Armand, M., & Taylor, R. H. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02162-7 · Cited by 81 · see citing works
“The differentiable dice score [28] is computed for each class and then averaged.”
MethodMachine learning in prostate MRI for prostate cancer: current status and future opportunities
Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020289 · Cited by 74 · see citing works
Segment Anything in Medical Images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2023) · arXiv · doi:10.48550/arxiv.2304.12306 · Cited by 67 · see citing works
Secure and robust machine learning for healthcare: A survey
Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3013489 · Cited by 59 · see citing works
Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation
Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., & Vercauteren, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_33 · Cited by 60 · see citing works
Wang, C., Li, Y., Tsuboshita, Y., Sakurai, T., Goto, T., Yamaguchi, H., Yamashita, Y., Sekiguchi, A., Tachimori, H., Initiative, F. T. A. D. N., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00577-x · Cited by 51 · see citing works
[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images
Sreelakshmy, R., Titus, A., Sasirekha, N., Logashanmugam, E., Begam, R. B., Ramkumar, G., & Raju, R. (2022) · BioMed Research International · doi:10.1155/2022/8342767 · Cited by 51 · see citing works
RetractedInteractive 3D U-net for the segmentation of the pancreas in computed tomography scans
Boers, T. G. W., Hu, Y., Gibson, E., Barratt, D. C., Bonmati, E., Krdzalic, J., van der Heijden, F., Hermans, J. J., & Huisman, H. J. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab6f99 · Cited by 51 · see citing works
Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning
Luo, J., Molbay, M., Chen, Y., Horvath, I., Kadletz, K., Kick, B., Zhao, S., Al-Maskari, R., Singh, I., Ali, M., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02528-1 · Cited by 53 · see citing works
Bridge, C. P., Rosenthal, M., Wright, B., Kotecha, G., Fintelmann, F., Troschel, F., Miskin, N., Desai, K., Wrobel, W., Babic, A., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01201-4_22 · Cited by 69 · see citing works
Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair
Breininger, K., Albarqouni, S., Kurzendorfer, T., Pfister, M., Kowarschik, M., & Maier, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1779-6 · Cited by 38 · see citing works
Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · see citing works
Looney, P., Yin, Y., Collins, S. L., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Stevenson, G. N. (2021) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2021.3052143 · Cited by 40 · see citing works
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Shin, U., Lee, K., Kweon, I. S., & Oh, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611200 · Cited by 49 · see citing works
“The supervised loss is defined as follows: where the mask loss L mask is a combination of binary cross-entropy loss and dice loss [27], defined as Modality-wise Supervision.”
BackgroundWang, C., Tachimori, H., Yamaguchi, H., Sekiguchi, A., Li, Y., Yamashita, Y., & Initiative, F. A. D. N. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02819-w · Cited by 39 · see citing works
Endometrium segmentation on transvaginal ultrasound image using key-point discriminator
Park, H., Lee, H. J., Kim, H. G., Ro, Y. M., Shin, D., Lee, S. R., Kim, S. H., & Kong, M. (2019) · Medical Physics · doi:10.1002/mp.13677 · Cited by 37 · see citing works
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35 · see citing works
Alzubaidi, M., Agus, M., Shah, U., Makhlouf, M., Alyafei, K., & Househ, M. (2022) · Diagnostics · doi:10.3390/diagnostics12092229 · Cited by 35 · see citing works
Zeng, W., Luo, J., Cheng, J., & Lu, Y. (2022) · Medical Physics · doi:10.1002/mp.15700 · Cited by 31 · see citing works
Luo, G., Dong, S., Wang, W., Wang, K., Cao, S., Tam, C., Zhang, H., Howey, J., Ohorodnyk, P., & Li, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101591 · Cited by 30 · see citing works
Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression
Lu, Y., Zhi, D., Zhou, M., Lai, F., Chen, G., Ou, Z., Zeng, R., Long, S., Qiu, R., Zhou, M., et al. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/5192338 · Cited by 30 · see citing works
Torrents-Barrena, J., López-Velazco, R., Piella, G., Masoller, N., Valenzuela-Alcaraz, B., Gratacós, E., Eixarch, E., Ceresa, M., & Ángel González Ballester, M. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.104993 · Cited by 25 · see citing works
Zhou, Y., Mei, S., Wang, J., Xu, Q., Zhang, Z., Qin, S., Feng, J., Li, C., Xing, S., Wang, W., et al. (2024) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102772 · Cited by 26 · see citing works
van den Noort, F., Manzini, C., van der Vaart, C. H., van Limbeek, M. A. J., Slump, C. H., & Grob, A. T. M. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.24810 · Cited by 25 · see citing works
Efficient and Lightweight Framework for Real-Time Ore Image Segmentation Based on Deep Learning
Sun, G., Huang, D., Cheng, L., Jia, J., Xiong, C., & Zhang, Y. (2022) · Minerals · doi:10.3390/min12050526 · Cited by 24 · see citing works
A Survey on Deep Learning for Precision Oncology
Wang, C.-W., Khalil, M.-A., & Firdi, N. (2022) · Diagnostics · doi:10.3390/diagnostics12061489 · Cited by 23 · see citing works
Fully Automated Analysis of Muscle Architecture from B-Mode Ultrasound Images with DL_Track_US
Ritsche, P., Franchi, M. V., Faude, O., Finni, T., Seynnes, O., & Cronin, N. J. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.10.011 · Cited by 20 · see citing works
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Shit, S., Paetzold, J. C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J. P. W., Bauer, U., & Menze, B. H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01629 · Cited by 60 · see citing works
“As baselines, we use the same architectures trained using soft-Dice [27, 45].”
MethodMartinez, C., Potter, K. M., Smith, M. D., Donahue, E. A., Collins, L., Korbin, J. P., & Roberts, S. A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00066 · Cited by 10 · see citing works
ArtiLatent: Realistic Articulated 3D Object Generation via Structured Latents
Chen, H., Lan, Y., Chen, Y., & Pan, X. (2025) · Proceedings of the SIGGRAPH Asia 2025 Conference Papers · doi:10.1145/3757377.3763922 · Cited by 4 · see citing works
A deep-learning framework reveals whole-body perturbations at cell level
Kaltenecker, D., Horvath, I., Al-Maskari, R., Chen, Y., Kolabas, Z. I., Hoeher, L., Todorov, M., Minde, D.-P., Kapoor, S., Turhan, S. G., et al. (2026) · Nature · doi:10.1038/s41586-026-10535-2 · Cited by 5 · see citing works
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Gopalakrishnan, V., Chlorogiannis, D.-D., Abumoussa, A., Larson, A. M., Haouchine, N., Orbach, D. B., Frisken, S., Dey, N., & Golland, P. (2025) · arXiv · doi:10.48550/arxiv.2503.16309 · Cited by 2 · see citing works
Qian, C., Yang, H., & Li, J. (2023) · 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc53992.2023.10393973 · Cited by 1 · see citing works
Skin Tokens: A Learned Compact Representation for Unified Autoregressive Rigging
Zhang, J.-P., Pu, C.-F., Guo, M.-H., Cao, Y.-P., & Hu, S.-M. (2026) · arXiv · doi:10.48550/arxiv.2602.04805
“To counteract this, we incorporate the Dice loss [Milletari et al. 2016], a metric widely used in image segmentation that is equivalent to the F1-score and focuses supervision on positive (non-zero) samples: where a small constant 𝜀 = 10 − 4 ensures numerical stability.”
Wali, S., Khan, M. I., Samina, S., & Raza Sialvi, M. A. (2026) · 2026 11th International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc70586.2026.11686729
- Authors
- Milletari, F., Navab, N., & Ahmadi, S.-A.
- Affiliations
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
- Venue
- 2016 Fourth International Conference on 3D Vision (3DV)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 565-571
- OpenAlex
- W2432481613
- Cited by
- 11,931
- References
- 20
- Citations by year
- 2026: 1261 · 2025: 1816 · 2024: 1880 · 2023: 1646 · 2022: 1541 · 2021: 1442 · 2020: 1071 · 2019: 812 · 2018: 348 · 2017: 98 · 2016: 7 · 2015: 1 · 2012: 3
- FWCI
- 146.48
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on 3D Vision (3DV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Funders
- Nvidia (K40 GPU)
- 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
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- 3D medical image segmentation, fully convolutional network, prostate MRI segmentation, class imbalance, data augmentation, histogram matching, end-to-end learning
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.
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
