State-of-the-art medical image segmentation methods based on various challenges! (Updated 201908)

Contents

Head

• 2019 MICCAI: Multimodal Brain Tumor Segmentation Challenge (BraTS2019) (Ongoing!!!)
• 2019 MICCAI: Automatic Structure Segmentation for Radiotherapy Planning Challenge (Ongoing!!!)
• 2018 MICCAI: Multimodal Brain Tumor Segmentation Challenge
• 2018 MICCAI: Ischemic stroke lesion segmentation
• 2018 MICCAI Grand Challenge on MR Brain Image Segmentation

Chest & Abdomen

• 2019 MICCAI: VerSe2019: Large Scale Vertebrae Segmentation Challenge (Ongoing!!!)
• 2019 MICCAI: Multi-sequence Cardiac MR Segmentation Challenge
• 2018 MICCAI: Left Ventricle Full Quantification Challenge 
• 2018 MICCAI: Atrial Segmentation Challenge
• 2019 MICCAI: Kidney Tumor Segmentation Challenge
• 2019 ISBI: Segmentation of THoracic Organs at Risk in CT images
• 2017 ISBI & MICCAI: Liver tumor segmentation challenge 
• 2012 MICCAI: Prostate MR Image Segmentation 

Others

• 2018 MICCAI Medical Segmentation Decathlon
• Awesome Open Source Tools
• Loss functions for class imbalanced Problems

Brain

• 2019 MICCAI: Multimodal Brain Tumor Segmentation Challenge (BraTS2019) (Ongoing!!!)
• 2019 MICCAI: 6-month Infant Brain MRI Segmentation from Multiple Sites (iSeg2019) (Ongoing!!!)

Heart

2019 MICCAI: Multi-sequence Cardiac MR Segmentation Challenge (MS-CMRSeg)

Waiting for results

Multi-sequence ventricle and myocardium segmentation.

Chest and Abdomen

2019 MICCAI: Kidney Tumor Segmentation Challenge (KiTS19)

Leaderboard (2019/07/30)

Date	First Author	Title	Composite Dice	Kidney Dice	Tumor Dice	Remark
20190730	Fabian Isensee	An attempt at beating the 3D U-Net (paper)	0.9123	0.9737	0.8509	1st Place
20190730	Xiaoshuai Hou	Cascaded Semantic Segmentation for Kidney and Tumor (paper)	0.9064	0.9674	0.8454	2nd Place
20190730	Guangrui Mu	Segmentation of kidney tumor by multi-resolution VB-nets (paper)	0.9025	0.9729	0.8321	3rd Place

2019 ISBI: Segmentation of THoracic Organs at Risk in CT images (SegTHOR)

Date	First Author	Title	Esophagus	Heart	Trachea	Aorta
20190320	Miaofei Han	Segmentation of CT thoracic organs by multi-resolution VB-nets (paper)	86	95	92	94
20190606	Shadab Khan	Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network (paper)	89.87	95.97	91.87	94

Challenge results

2017 ISBI & MICCAI: Liver tumor segmentation challenge (LiTS)

Summary: The Liver Tumor Segmentation Benchmark (LiTS), Patrick Bilic et al. 201901 (arxiv)

Date	First Author	Title	Liver Dice	Tumor Dice
201709	Xiaomeng Li	H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, (paper), (Keras code)	0.961	0.722

2012 MICCAI: Prostate MR Image Segmentation (PROMISE12)

Date	First Author	Title	Whole Dice	Overall Score
201904	Anonymous	3D segmentation and 2D boundary network (paper)	-	90.34
201902	Qikui Zhu	Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (paper)	91.41	89.59

Others

2018 MICCAI Medical Segmentation Decathlon

Task	Data Info	Fabian Isensee et al. (paper)	Yingda Xia et al. (paper)
Brats	Multimodal multisite MRI data (FLAIR, T1w, T1gd,T2w), (484 Training + 266 Testing)	0.68/0.48/0.68	0.675/0.45/0.68
Heart	Mono-modal MRI (20 Training + 10 Testing)	0.93	0.925
Hippocampus head and body	Mono-modal MRI (263 Training + 131 Testing)	0.90/0.89	0.88/0.867
Liver & Tumor	Portal venous phase CT (131 Training + 70 Testing)	0.95/0.74	0.95/0.714
Lung	CT (64 Training + 32 Testing)	0.69	0.52
Pancreas & Tumor	Portal venous phase CT (282 Training +139 Testing)	0.80/0.52	0.784/0.385
Prostate central gland and peripheral	Multimodal MR (T2, ADC) (32 Training + 16 Testing)	0.76/0.90	0.69/0.867
Hepatic vessel& Tumor	CT, (303 Training + 140 Testing)	0.63/0.69	-
Spleen	CT (41 Training + 20 Testing)	0.96	-
Colon	CT (41 Training + 20 Testing)	0.56	-

Only showing Dice Score.

Recent papers on Medical Segmentation Decathlon

Date	First Author	Title	Score
20190606	Zhuotun Zhu	V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation (arxiv)	Lung tumor: 55.27; Pancreas and tumor: 79.94, 37.78 (4-fold CV)

Past Challenges (New submission closed)

2018 MICCAI: Multimodal Brain Tumor Segmentation Challenge(BraTS)

Summary: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Spyridon Bakas et al. 201811, (arxiv)

Rank(18)	First Author	Title	Val. WT/EN/TC Dice	Test Val. WT/ET/TC Dice
1	Andriy Myronenko	3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (paper)	0.91/0.823/0.867	0.884/0.766/0.815
2	Fabian Isensee	No New-Net (paper)	0.913/0.809/0.863	0.878/0.779/0.806
3	Richard McKinley	Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation (paper)	0.903/0.796/0.847	0.886/0.732/0.799
3	Chenhong Zhou	Learning Contextual and Attentive Information for Brain Tumor Segmentation (paper)	0.9095/0.8136/0.8651	0.8842/0.7775/0.7960
New	Xuhua Ren	Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation (paper)	0.915/0.832/0.883	-

2018 MICCAI: Ischemic stroke lesion segmentation (ISLES )

Date	First Author	Title	Dice
20190605	Yu Chen	OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images (paper)	57.90 (5-fold CV)
201812	Hoel Kervadec	Boundary loss for highly unbalanced segmentation (paper), (pytorch 1.0 code)	65.6
201809	Tao Song	3D Multi-scale U-Net with Atrous Convolution for Ischemic Stroke Lesion Segmentation, (paper)	55.86
201809	Pengbo Liu	Stroke Lesion Segmentation with 2D Convolutional Neutral Network and Novel Loss Function, (paper)	55.23
201809	Yu Chen	Ensembles of Modalities Fused Model for Ischemic Stroke Lesion Segmentation, (paper)	-

2018 MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS18)

• Eight Label Segmentation Results (201809)

Rank	First Author	Title	Score
1	Miguel Luna	3D Patchwise U-Net with Transition Layers for MR Brain Segmentation (paper)	9.971
2	Alireza Mehrtash	U-Net with various input combinations (paper)	9.915
3	Xuhua Ren	Ensembles of Multiple Scales, Losses and Models for Segmentation of Brain Area (paper)	9.872
201906	Xuhua Ren	Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization (arxiv )	5 fold CV Dice: 84.46

• Three Label Segmentation Results (201809)

Rank	First Author	Title	GM/WM/CSF Dice	Score
1	Liyan Sun	Brain Tissue Segmentation Using 3D FCN with Multi-modality Spatial Attention (paper)	0.86/0.889/0.850	11.272

2018 MICCAI: Left Ventricle Full Quantification Challenge (LVQuan18)

Rank	First Author	Title
1	Jiahui Li	Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning, (paper)
2	Eric Kerfoot	Left-Ventricle Quantification Using Residual U-Net, (paper)
3	Fumin Guo	Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-Net and Continuous Max-Flow (paper)

2018 MICCAI: Atrial Segmentation Challenge (AtriaSeg)

Rank	First Author	Title	Score
1	Qing Xia	Automatic 3D Atrial Segmentation from GE-MRIs Using Volumetric Fully Convolutional Networks (paper)	0.932
2	Cheng Bian	Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation (paper)	0.926
2	Sulaiman Vesal	Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MR (paper)	0.926

Awesome Open Source Tools

Task	First Author	Title	Notes
Detection&Segmentation	Paul F. Jaeger	Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection, (paper), (code)	pytorch 0.4
Medical Image Analysis	Eli Gibson and Wenqi Li	NiftyNet: a deep-learning platform for medical imaging (paper), (code)	Tensorflow 1.12
Segmentation	Christian S. Perone	MedicalTorch	pytorch>=0.4
awesome-semantic-segmentation	mrgloom	awesome-semantic-segmentation	3000+ stars
Segmentation	Fabian Isensee	nnU-Net (paper) (code)	100+stars

Loss functions for Imbalanced Problems

Date	First Author	Title	Conference/Journal
201904	Davood Karimi	Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks (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 code)	MIDL 2019
201810	Nabila Abraham	A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation (paper)	ISBI 2019
201809	Fabian Isensee	CE+Dice nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (paper)	arxiv
201808	Ken C. L. Wong	3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes (paper)	MICCAI 2018
201806	Javier Ribera	Weighted Hausdorff Distance: Locating Objects Without Bounding Boxes (paper), (pytorch code)	CVPR 2019
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
201511	Tom Brosch	"Sensitivity-Specifity loss" Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation (paper)	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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