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A collection of loss functions for medical image segmentation

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Loss functions for image segmentation

A collection of loss functions for medical image segmentation

Date First Author Title Conference/Journal
202003 Suprosanna Shit clDice -- a Topology-Preserving Loss Function for Tubular Structure Segmentation (pytorch) arXiv
202002 TBD Uncertainty-weighted Loss Function for Medical Image Segmentation using Deep Convolutional Neural Network (paper) MIDL 2020
201912 Yuan Xue Shape-Aware Organ Segmentation by Predicting Signed Distance Maps (arxiv) AAAI 2020
201912 Xiaoling Hu Topology-Preserving Deep Image Segmentation (paper) NeurIPS
201912 JohannesC.Paetzold clDice-a Novel Connectivity-Preserving Loss Function for Vessel Segmentation (paper) MedNeurIPS2019
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) 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
201606 Fausto Milletari "Dice Loss" V-net: Fully convolutional neural networks for volumetric medical image segmentation (paper), (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 code can be found here.

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A collection of loss functions for medical image segmentation


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