Loss odyssey in medical image segmentation

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

Adam: A Method for Stochastic Optimization

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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

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Advances in neural information processing systems 7

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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

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nnU-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

Focal Loss for Dense Object Detection

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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 · see citing works

The multimodal brain tumor image segmentation benchmark (BRATS)

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Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations

Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829 · see citing works

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., & Heng, P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,530 · see citing works

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

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Attention gated networks: learning to leverage salient regions in medical images

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International Conference on Learning Representations (ICLR)

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Medical Image Computing and Computer-Assisted Intervention

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

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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

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. (2019) · arXiv · doi:10.48550/arxiv.1901.04056 · Cited by 1,412 · see citing works

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

(2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9 · Cited by 1,149 · see citing works

Visualizing the Loss Landscape of Neural Nets

Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2017) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000461393 · Cited by 1,148 · see citing works

Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks

Salehi, S. S. M., Deniz Erdogmus, B., & Gholipour, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67389-9_44 · Cited by 1,137 · see citing works

The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks

Berman, M., Triki, A. R., & Blaschko, M. B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00464 · Cited by 1,114 · see citing works

Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation

Rahman, M. A., & Wang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_22 · Cited by 1,015 · see citing works

A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

Abraham, N., & Khan, N. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759329 · Cited by 982 · see citing works

Weighted Res-UNet for High-Quality Retina Vessel Segmentation

Xiao, X., Lian, S., Luo, Z., et al. (2018) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme.2018.00080 · Cited by 970 · see citing works

2016 Fourth International Conference on 3D Vision (3DV)

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A large annotated medical image dataset for the development and evaluation of segmentation algorithmsPreprint

Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp‐Schneider, A., Landman, B. A., Litjens, G. J. S., Menze, B. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09063 · Cited by 703 · see citing works

Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks

Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K. S., Davidson, B. R., Pereira, S. P., Clarkson, M. J., & Barratt, D. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2806309 · Cited by 701 · see citing works

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks

Karimi, D., & Salcudean, S. E. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2930068 · Cited by 656 · see citing works

Boundary loss for highly unbalanced segmentation

Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, É., Dolz, J., & Ayed, I. B. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101851 · Cited by 645 · see citing works

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

Heller, N. E., Isensee, F., Maier-Hein, K. H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101821 · Cited by 586 · see citing works

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2912935 · Cited by 538 · see citing works

AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

Zhu, W., Huang, Y., Zeng, L., Chen, X., Liu, Y., Qian, Z. Y., Du, N., Fan, W., & Xie, X. S. (2018) · Medical Physics · doi:10.1002/mp.13300 · Cited by 511 · see citing works

3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationPreprint

Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06650 · Cited by 488 · see citing works

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

Supatto, W. (2020) · doi:10.1109/isbi45749.2020 · Cited by 472 · see citing works

Combo loss: Handling input and output imbalance in multi-organ segmentation

Taghanaki, S. A., Zheng, Y., Zhou, S. K., Georgescu, B., Sharma, P., Xu, D., Comaniciu, D., & Hamarneh, G. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.04.005 · Cited by 463 · see citing works

Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index

Eelbode, T., Bertels, J., Berman, M., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3002417 · Cited by 423 · see citing works

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Zhuang, X., Li, L., Payer, C., Štern, D., Urschler, M., Heinrich‬, M. P., Oster, J., Wang, C., Smedby, Ö., Bian, C., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101537 · Cited by 369 · see citing works

Deep Learning and Data Labeling for Medical Applications

Cornebise, J., Cardoso, J. S., Lu, Z., Loog, M., Nascimento, J. C., Papa, J. P., Belagiannis, V., Tavares, J. M. R. S., Bradley, A. P., Peter, L., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8 · Cited by 351 · see citing works

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapyPreprint

Nikolov, S., Blackwell, S., Zverovitch, A., Mendes, R. L., Livne, M., Fauw, J. D., Patel, Y., Meyer, C., Askham, H., Romera‐Paredes, B., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04430 · Cited by 239 · see citing works

Asymmetric Loss Functions and Deep Densely-Connected Networks for Highly-Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

Hashemi, S. R., Salehi, S. S. M., Deniz Erdogmus, B., Prabhu, S. P., Warfield, S. K., & Gholipour, A. (2018) · IEEE Access · doi:10.1109/access.2018.2886371 · Cited by 216 · see citing works

3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes

Wong, K. C. L., Moradi, M., Tang, H., & Syeda-Mahmood, T. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_70 · Cited by 200 · see citing works

Topology-Preserving Deep Image SegmentationPreprint

Hu, X., Li, F., Samaras, D., & Chen, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05404 · Cited by 164 · see citing works

Bridging Category-level and Instance-level Semantic Image SegmentationPreprint

Wu, Z., Shen, C., & van den Hengel, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06885 · Cited by 148 · see citing works

nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.Preprint

Isensee, F., Petersen, J., Köhl, S., Jäger, P. F., & Maier-Hein, K. H. (2019) · arXiv (Cornell University) · Cited by 144 · see citing works

The Importance of Skip Connections in Biomedical Image SegmentationPreprint

Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04117 · Cited by 132 · see citing works

Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

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2018 9th International Conference on Information Technology in Medicine and Education (ITME)

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How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

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Removing segmentation inconsistencies with semi-supervised non-adjacency constraint

Ganaye, P.-A., Sdika, M., Triggs, B., & Benoit-Cattin, H. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101551 · Cited by 37 · see citing works

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

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Distance Map Loss Penalty Term for Semantic SegmentationPreprint

Calivá, F., Iriondo, C., Martinez, A. M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03679 · Cited by 24 · see citing works

Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions

Seo, H., Bassenne, M., & Xing, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3031913 · Cited by 24 · see citing works

Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation

Shirokikh, B., Shevtsov, A. E., Kurmukov, A. G., Dalechina, A., Krivov, E., Kostjuchenko, V. V., Голанов, А. В., & Belyaev, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_51 · Cited by 21 · see citing works

Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a Novel Penalty Loss Function

Yang, S., Kweon, J., & Kim, Y. (2019) · Cited by 18 · 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

Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images

Zhang, J., Petitjean, C., & Ainouz, S. (2022) · Journal of Imaging · doi:10.3390/jimaging8020023 · Cited by 21 · see citing works

Coups and Games with Dice

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Random Dice Roller

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On Device of Octahedron Dice

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Optimal strategies for equal-sum dice games

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A study of a dice problem

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