Loss functions for image segmentation
A collection of loss functions for medical 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 |
|---|---|---|---|
| 20210418 | Bingyuan Liu | The hidden label-marginal biases of segmentation losses (pytorch) | arxiv |
| 20210330 | Suprosanna Shit and Johannes C. Paetzold | clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation (keras and pytorch) | CVPR 2021 |
| 20210325 | Attila Szabo, Hadi Jamali-Rad | Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation | arxiv |
| 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.
