Loss functions for image segmentation
@article{LossOdyssey,
title = {Loss Odyssey in Medical Image Segmentation},
journal = {Medical Image Analysis},
volume = {71},
pages = {102035},
year = {2021},
author = {Jun Ma and Jianan Chen and Matthew Ng and Rui Huang and Yu Li and Chen Li and Xiaoping Yang and Anne L. Martel}
doi = {https://doi.org/10.1016/j.media.2021.102035},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521000815}
}
Take-home message: compound loss functions are the most robust losses, especially for the highly imbalanced segmentation tasks.
Some recent side evidence: the winner in MICCAI 2020 HECKTOR Challenge used DiceFocal loss; the winner and runner-up in MICCAI 2020 ADAM Challenge used DiceTopK loss.
| Date | First Author | Title | Conference/Journal |
|---|---|---|---|
| 20210330 | Suprosanna Shit and Johannes C. Paetzold | clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation (keras and pytorch) | CVPR 2021 |
| 20210318 | Xiaoling Hu | Topology-Aware Segmentation Using Discrete Morse Theory arxiv | ICLR 2021 |
| 20210211 | Hoel Kervadec | Beyond pixel-wise supervision: semantic segmentation with higher-order shape descriptors | Submitted to MIDL 2021 |
| 20210210 | Rosana EL Jurdi | A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation | Submitted to MIDL 2021 |
| 20201222 | Zeju Li | Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation | TMI |
| 20210129 | Nick Byrne | A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI arxiv | STACOM 2020 |
| 20201019 | Hyunseok Seo | Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions | TMI |
| 20200929 | Stefan Gerl | A Distance-Based Loss for Smooth and Continuous Skin Layer Segmentation in Optoacoustic Images | MICCAI 2020 |
| 20200821 | Nick Byrne | A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI arxiv | STACOM |
| 20200720 | Boris Shirokikh | Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation arxiv (pytorch) | MICCAI 2020 |
| 20200708 | Gonglei Shi | Marginal loss and exclusion loss for partially supervised multi-organ segmentation (arXiv) | MedIA |
| 20200706 | Yuan Lan | An Elastic Interaction-Based Loss Function for Medical Image Segmentation (pytorch) (arXiv) | MICCAI 2020 |
| 20200615 | Tom Eelbode | Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index | TMI |
| 20200605 | Guotai Wang | Noise-robust Dice loss: A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images (pytorch) | TMI |
| 202004 | J. H. Moltz | Contour Dice coefficient (CDC) Loss: Learning a Loss Function for Segmentation: A Feasibility Study | ISBI |
| 201912 | Yuan Xue | Shape-Aware Organ Segmentation by Predicting Signed Distance Maps (arxiv) (pytorch) | AAAI 2020 |
| 201912 | Xiaoling Hu | Topology-Preserving Deep Image Segmentation (paper) (pytorch) | NeurIPS |
| 201910 | Shuai Zhao | Region Mutual Information Loss for Semantic Segmentation (paper) (pytorch) | NeurIPS 2019 |
| 201910 | Shuai Zhao | Correlation Maximized Structural Similarity Loss for Semantic Segmentation (paper) | arxiv |
| 201908 | Pierre-AntoineGanaye | Removing Segmentation Inconsistencies with Semi-Supervised Non-Adjacency Constraint (paper) (official pytorch) | Medical Image Analysis |
| 201906 | Xu Chen | Learning Active Contour Models for Medical Image Segmentation (paper) (official-keras) | CVPR 2019 |
| 20190422 | Davood Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks (paper) (pytorch) | TMI 201907 |
| 20190417 | Francesco Caliva | Distance Map Loss Penalty Term for Semantic Segmentation (paper) | MIDL 2019 |
| 20190411 | Su Yang | Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a Novel Penalty Loss Function (paper) | MIDL 2019 |
| 20190405 | Boah Kim | Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning (paper) | arxiv |
| 201901 | Seyed Raein Hashemi | Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection (paper) | IEEE Access |
| 201812 | Hoel Kervadec | Boundary loss for highly unbalanced segmentation (paper), (pytorch 1.0) | MIDL 2019 |
| 201810 | Nabila Abraham | A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation (paper) (keras) | ISBI 2019 |
| 201809 | Fabian Isensee | CE+Dice: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (paper) | arxiv |
| 20180831 | Ken C. L. Wong | 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes (paper) | MICCAI 2018 |
| 20180815 | Wentao Zhu | Dice+Focal: AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy (arxiv) (pytorch) | Medical Physics |
| 201806 | Javier Ribera | Weighted Hausdorff Distance: Locating Objects Without Bounding Boxes (paper), (pytorch) | CVPR 2019 |
| 201805 | Saeid Asgari Taghanaki | Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (arxiv) (keras) | Computerized Medical Imaging and Graphics |
| 201709 | S M Masudur Rahman AL ARIF | Shape-aware deep convolutional neural network for vertebrae segmentation (paper) | MICCAI 2017 Workshop |
| 201708 | Tsung-Yi Lin | Focal Loss for Dense Object Detection (paper), (code) | ICCV, TPAMI |
| 20170711 | Carole Sudre | Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (paper) | DLMIA 2017 |
| 20170703 | Lucas Fidon | Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks (paper) | MICCAI 2017 BrainLes |
| 201705 | Maxim Berman | The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks (paper), (code) | CVPR 2018 |
| 201701 | Seyed Sadegh Mohseni Salehi | Tversky loss function for image segmentation using 3D fully convolutional deep networks (paper) | MICCAI 2017 MLMI |
| 201612 | Md Atiqur Rahman | Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation (paper) | 2016 International Symposium on Visual Computing |
| 201608 | Michal Drozdzal | "Dice Loss (without square)" The Importance of Skip Connections in Biomedical Image Segmentation (arxiv) | DLMIA 2016 |
| 201606 | Fausto Milletari | "Dice Loss (with square)" V-net: Fully convolutional neural networks for volumetric medical image segmentation (arxiv), (caffe code) | International Conference on 3D Vision |
| 201605 | Zifeng Wu | TopK loss Bridging Category-level and Instance-level Semantic Image Segmentation (paper) | arxiv |
| 201511 | Tom Brosch | "Sensitivity-Specifity loss" Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation (paper) (code) | MICCAI 2015 |
| 201505 | Olaf Ronneberger | "Weighted cross entropy" U-Net: Convolutional Networks for Biomedical Image Segmentation (paper) | MICCAI 2015 |
| 201309 | Gabriela Csurka | What is a good evaluation measure for semantic segmentation? (paper) | BMVA 2013 |
Most of the corresponding tensorflow code can be found here.
32 Nov 17, 2022
23 Jul 02, 2022
14 Jul 24, 2022
117 Dec 15, 2022
20 May 22, 2022
35 Sep 08, 2021
2 Jan 08, 2022
26 Nov 18, 2022
30 Dec 18, 2022
1 Feb 09, 2022
57 Nov 12, 2022
35 Oct 04, 2022
150 Dec 26, 2022
4 Oct 23, 2022
208 Jan 03, 2023
1 Jan 13, 2022
146 Dec 22, 2022
4 Aug 13, 2022
11 Dec 26, 2022
7 Nov 08, 2022