# SegLoss

**Repository Path**: junma11/SegLoss

## Basic Information

- **Project Name**: SegLoss
- **Description**: A collection of loss functions for medical image segmentation
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Loss functions for image segmentation
A collection of loss functions for medical image segmentation

|Date|First Author|Title|Conference/Journal|
|---|---|---|---|
|201910|Shuai Zhao|Correlation Maximized Structural Similarity Lossfor Semantic Segmentation [(paper)](https://arxiv.org/pdf/1910.08711.pdf)|TBD|
|201908|Pierre-AntoineGanaye|Removing Segmentation Inconsistencies with Semi-Supervised Non-Adjacency Constraint [(paper)](https://www.sciencedirect.com/science/article/pii/S1361841519300866?dgcid=raven_sd_aip_email) [(official pytorch)](https://github.com/trypag/NonAdjLoss)|[Medical Image Analysis](https://www.sciencedirect.com/science/article/pii/S1361841519300866?dgcid=raven_sd_aip_email)|
|201906|Xu Chen|Learning **Active Contour Models** for Medical Image Segmentation [(paper)](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Active_Contour_Models_for_Medical_Image_Segmentation_CVPR_2019_paper.pdf) [(official-keras)](https://github.com/xuuuuuuchen/Active-Contour-Loss/blob/master/Active-Contour-Loss.py)|CVPR 2019|
|20190422|Davood Karimi|Reducing the **Hausdorff Distance** in Medical Image Segmentation with Convolutional Neural Networks [(paper)](https://arxiv.org/pdf/1904.10030v1.pdf)|[TMI 201907](https://ieeexplore.ieee.org/document/8767031)|
|20190417|Francesco Caliva|**Distance Map Loss** Penalty Term for Semantic Segmentation [(paper)](https://openreview.net/forum?id=B1eIcvS45V)|[MIDL 2019](http://2019.midl.io/)|
|20190411|Su Yang|Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a Novel **Penalty Loss Function** [(paper)](https://openreview.net/forum?id=H1lTh8unKN)|[MIDL 2019](http://2019.midl.io/)|
|20190405|Boah Kim|Multiphase **Level-Set Loss** for Semi-Supervised and Unsupervised Segmentation with Deep Learning [(paper)](https://arxiv.org/pdf/1904.02872.pdf)|arxiv|
|201901|[Seyed Raein Hashemi](https://scholar.google.ca/citations?user=4VEP0fsAAAAJ&hl=en&oi=sra)|**Asymmetric Loss** Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection [(paper)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8573779)|IEEE Access|
|201812|[Hoel Kervadec](https://scholar.google.ca/citations?user=yeFGhfgAAAAJ&hl=zh-CN&oi=sra)|**Boundary loss** for highly unbalanced segmentation [(paper)](https://arxiv.org/pdf/1812.07032.pdf), [(pytorch 1.0)](https://github.com/LIVIAETS/surface-loss)|[MIDL 2019](http://2019.midl.io/)|
|201810|[Nabila Abraham](https://scholar.google.ca/citations?user=OOvooSMAAAAJ&hl=zh-CN&oi=sra)|A Novel **Focal Tversky loss** function with improved Attention U-Net for lesion segmentation [(paper)](https://arxiv.org/pdf/1810.07842.pdf) [(keras)](https://github.com/nabsabraham/focal-tversky-unet)|[ISBI 2019](https://biomedicalimaging.org/2019/)|
|201809|[Fabian Isensee](https://scholar.google.com/citations?user=PjerEe4AAAAJ&hl=en)|**CE+Dice:** nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation [(paper)](https://arxiv.org/abs/1809.10486)|arxiv|
|20180831|[Ken C. L. Wong](https://scholar.google.ca/citations?hl=zh-CN&user=XjnODToAAAAJ&view_op=list_works&sortby=pubdate)|3D Segmentation with **Exponential Logarithmic Loss** for Highly Unbalanced Object Sizes [(paper)](https://arxiv.org/abs/1809.00076)|MICCAI 2018|
|20180815|[Wentao Zhu](https://www.ics.uci.edu/~wentaoz1/)|**Dice+Focal:** AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy [(arxiv)](https://arxiv.org/abs/1808.05238) [(pytorch)](https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation)|[Medical Physics](https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13300)|
|201806|[Javier Ribera](https://scholar.google.ca/citations?user=TAaovakAAAAJ&hl=zh-CN&oi=sra)|**Weighted Hausdorff Distance:** Locating Objects Without Bounding Boxes [(paper)](https://arxiv.org/abs/1806.07564), [(pytorch)](https://github.com/HaipengXiong/weighted-hausdorff-loss)|CVPR 2019|
|201805|Saeid Asgari Taghanaki|**Combo Loss:** Handling Input and Output Imbalance in Multi-Organ Segmentation [(arxiv)](https://arxiv.org/pdf/1805.02798.pdf) [(keras)](https://github.com/asgsaeid/ComboLoss/blob/master/combo_loss.py)|[Computerized Medical Imaging and Graphics](https://www.sciencedirect.com/science/article/abs/pii/S0895611118305688)|
|201709|[S M Masudur Rahman AL ARIF](https://scholar.google.ca/citations?user=6bgRPC8AAAAJ&hl=en&oi=sra)|**Shape-aware** deep convolutional neural network for vertebrae segmentation [(paper)](http://www.gregslabaugh.net/publications/ArifMSKI-MICCAI2017.pdf)|[MICCAI 2017 Workshop](https://link.springer.com/chapter/10.1007/978-3-319-74113-0_2)|
|201708|[Tsung-Yi Lin](https://scholar.google.ca/citations?user=_BPdgV0AAAAJ&hl=zh-CN&oi=sra)|**Focal Loss** for Dense Object Detection [(paper)](https://arxiv.org/abs/1708.02002), [(code)](https://github.com/facebookresearch/Detectron)|ICCV, TPAMI|
|20170711|[Carole Sudre](https://scholar.google.ca/citations?user=14GfvB4AAAAJ&hl=zh-CN&oi=sra)|**Generalised Dice** overlap as a deep learning loss function for highly unbalanced segmentations [(paper)](https://arxiv.org/abs/1707.03237)|DLMIA 2017|
|20170703|[Lucas Fidon](https://scholar.google.ca/citations?user=GORojioAAAAJ&hl=zh-CN&oi=sra)|**Generalised Wasserstein Dice** Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks [(paper)](https://arxiv.org/abs/1707.00478)|MICCAI 2017 BrainLes|
|201705|[Maxim Berman](https://scholar.google.ca/citations?user=RoOng2wAAAAJ&hl=zh-CN&oi=sra)|The **Lovász-Softmax loss:** A tractable surrogate for the optimization of the intersection-over-union measure in neural networks [(paper)](https://arxiv.org/abs/1705.08790), [(code)](https://github.com/bermanmaxim/LovaszSoftmax)|CVPR 2018|
|201701|[Seyed Sadegh Mohseni Salehi](https://scholar.google.ca/citations?user=hTWINokAAAAJ&hl=zh-CN&oi=sra)|**Tversky loss** function for image segmentation using 3D fully convolutional deep networks [(paper)](https://arxiv.org/abs/1706.05721)|MICCAI 2017 MLMI|
|201612|[Md Atiqur Rahman](https://scholar.google.ca/citations?user=tLPerVUAAAAJ&hl=zh-CN&oi=sra)|Optimizing **Intersection-Over-Union** in Deep Neural Networks for Image Segmentation [(paper)](https://www.cs.umanitoba.ca/~ywang/papers/isvc16.pdf)|[2016 International Symposium on Visual Computing](https://link.springer.com/chapter/10.1007/978-3-319-50835-1_22)|
|201606|[Fausto Milletari](https://faustomilletari.github.io/)|**"Dice Loss"** V-net: Fully convolutional neural networks for volumetric medical image segmentation [(paper)](https://arxiv.org/abs/1606.04797), [(caffe code)](https://github.com/faustomilletari/VNet)|International Conference on 3D Vision|
|201605|Zifeng Wu|**TopK loss** Bridging Category-level and Instance-level Semantic Image Segmentation [(paper)](https://arxiv.org/abs/1605.06885)|arxiv|
|201511|[Tom Brosch](https://scholar.google.ca/citations?user=KChq7WIAAAAJ&hl=zh-CN&oi=sra)|**"Sensitivity-Specifity loss"** Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation [(paper)](http://www.rogertam.ca/Brosch_MICCAI_2015.pdf) [(code)](https://github.com/NifTK/NiftyNet/blob/df0f86733357fdc92bbc191c8fec0dcf49aa5499/niftynet/layer/loss_segmentation.py#L392)|[MICCAI 2015](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_1)|
|201505|[Olaf Ronneberger](https://scholar.google.ca/citations?user=7jrO1NwAAAAJ&hl=zh-CN&oi=sra)|**"Weighted cross entropy"** U-Net: Convolutional Networks for Biomedical Image Segmentation [(paper)](https://arxiv.org/abs/1505.04597)|[MICCAI 2015](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28)|
|201309|[Gabriela Csurka](https://scholar.google.ca/citations?user=PXm1lPAAAAAJ&hl=zh-CN&oi=sra)|What is a good evaluation measure for semantic segmentation? [(paper)](http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf)|BMVA 2013|

> Most of the corresponding code can be found [here](https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py).