Awesome medical image segmentation methods based on various challenges! (Updated 2023-12)

Overview of medical image segmentation challenges in MICCAI 2023.

For each competition, we present the segmentation target, image modality, dataset size, and the base network architecture in the winning solution. The competitions cover different modalities and segmentation targets with various challenging characteristics. U-Net and its variants still dominate the winning solutions.

Contents

Head and Neck

• Brain Tumor Segmentation: BraTS 2019, 2020, 2021, 2022
• Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (INSTANCE)
• Retinal Fundus Glaucoma Challenge Edition2 (REFUGE2)
• CATARACTS Semantic Segmentation
• Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images (ABCs)
• 3D Head and Neck Tumor Segmentation: HECKTOR 2020, 2021, 2022
• Cerebral Aneurysm Segmentation (CADA)
• Aneurysm Detection And segMenation Challenge 2020 (ADAM)
• Thyroid nodule segmentation and classification challenge (TN-SCUI 2020)
• Automatic Lung Cancer Patient Management (LNDb) (LNDb)
• 6-month Infant Brain MRI Segmentation from Multiple Sites: iSeg2019, cSeg2022

Heart

• Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI (EMIDEC)
• Automated Segmentation of Coronary Arteries (ASOCA) (Results)
• MyoPS 2020: Myocardial pathology segmentation combining multi-sequence CMR (Homepage)

Chest & Abdomen

• Fast and low-resource abdominal organ segmentation: FLARE 2021, 2022
• Multi-Modality Abdominal Multi-Organ Segmentation Challenge (AMOS22) (Results)
• Kidney Tumor Segmentation Challenge: KiTS19, (KiTS21)
• Large Scale Vertebrae Segmentation Challenge: VerSe2019, VerSe2020

Others

• 2018 MICCAI: Medical Segmentation Decathlon (MSD) (Results)
• 2020 MICCAI: Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ) (Results)
• Awesome Open Source Tools
• Loss Odyssey in Medical Image Segmentation

Ongoing Challenges

2022 MICCAI: Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (INSTANCE)

Date	First Author	Title	DSC	NSD	RVD	HD	Remark
202301	Xiangyu Li	The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge (paper)	0.7912	0.5026	0.21	29.02	Summary paper

2022 MICCAI: Brain Tumor Segmentation (BraTS2022)

Date	First Author	Title	ET DSC	TC DSC	WT DSC
202209	Ramy A. Zeineldin	Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution (paper)	0.8438	0.8753	0.9271

2022 MICCAI: Multi-Modality Abdominal Multi-Organ Segmentation Challenge (AMOS22) (Results)

Date	First Author	Title	Task 1-DSC	Task 1-NSD	Task 2-DSC	Task 2-NSD	Remark
202209	Fabian Isensee, Constantin Ulrich and Tassilo Wald	Extending nnU-Net is all you need (paper) (code)	TBA	TBA	TBA	TBA	1st Place in MICCAI 2022
202303	Saikat Roy	MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation (paper) (code)	89.87	92.95	TBA	TBA	Improve nnUNet by ~1%

2021 ISBI: MitoEM Challenge: Large-scale 3D Mitochondria Instance Segmentation (MitoEM) (Results)

Date	First Author	Title	MitoEM-R	MitoEM-H	Average	Remark
202104	Mingxing Li	Advanced Deep Networks for 3D Mitochondria Instance Segmentation (paper) (code)	0.851	0.829	0.840	1st Place in ISBI 2021

2021 MICCAI: Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) (Results)

Date	First Author	Title	DSC	NSD	Time	GPU Memory	Remark
202110	Fan Zhang	Efficient Context-Aware Network for Abdominal Multi-organ Segmentation (paper) (code)	0.895	0.796	9.32	1177	1st Place in MICCAI 2021

2021 MICCAI: Kidney Tumor Segmentation Challenge (KiTS) (Results)

Date	First Author	Title	DSC	NSD	Remark
202110	Zhaozhong Chen	A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge (paper)	0.9077	0.8262	1st Place in MICCAI 2021

2020 MICCAI: Cerebral Aneurysm Segmentation (CADA) (Results)

Date	First Author	Title	IoU	HD	MD	Remark
20201008	Mediclouds	TBA	0.758	2.866	1.618	1st Place in MICCAI 2020
20201008	Jun Ma	Exploring Large Context for Cerebral Aneurysm Segmentation (arxiv) (Code)	0.759	4.967	3.535	2nd Place in MICCAI 2020

2020 MICCAI: Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI (EMIDEC)

Date	First Author	Title	Myo	Infarction	Re-flow	Remark
20201008	Yichi Zhang	Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI (arxiv)	0.8786	0.7124	0.7851	1st Place in MICCAI 2020
20201008	Jun Ma	Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI (arxiv)	0.8628	0.6224	0.7776	2nd Place in MICCAI 2020
20201008	Xue Feng	Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation (paper)	0.8356	0.4568	0.7222	3rd Place in MICCAI 2020

Metrics: DSC

Aneurysm Detection And segMenation Challenge 2020 (ADAM) (Results)

Date	First Author	Title	DSC	MHD	VS	Remark
20201008	Jun Ma	Loss Ensembles for Intracranial Aneurysm Segmentation: An Embarrassingly Simple Method (Code)	0.41	8.96	0.50	1st Place in MICCAI 2020
20201008	Yuexiang Li	Automatic Aneurysm Segmentation via 3D U-Net Ensemble	0.40	8.67	0.48	2nd Place in MICCAI 2020
20201008	Riccardo De Feo	Multi-loss CNN ensemblesfor aneurysm segmentation	0.28	18.13	0.39	3rd Place in MICCAI 2020

Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms) (Results)

Date	First Author	Title	LV	MYO	RV	Remark
20201004	Peter Full	The effect of Data Augmentation on Robustness against Domain Shifts in cMRI Segmentation	0.910	0.849	0.884	1st Place in MICCAI 2020
20201004	Yao Zhang	Semi-Supervised Cardiac Image Segmentation via Label Propagation and Style Transfer	0.906	0.840	0.878	2nd Place in MICCAI 2020
20201004	Jun Ma	Histogram Matching Augmentation for Domain Adaptation (code)	0.902	0.835	0.874	3rd Place in MICCAI 2020

Dice values are reported. Video records are available on pathable. All the papers are in press

2020 MICCAI: 3D Head and Neck Tumor Segmentation in PET/CT (HECKTOR 2020). (Results)

Date	First Author	Title	DSC	Remark
20201004	Andrei Iantsen	Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images (paper)	0.759	1st Place in MICCAI 2020
20201004	Jun Ma	Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET Images (paper)	0.752	2nd Place in MICCAI 2020

2020 MICCAI: Thyroid nodule segmentation and classification challenge (TN-SCUI 2020). (Results)

Date	First Author	Title	IoU	Remark
20201004	Mingyu Wang	A Simple Cascaded Framework for Automatically Segmenting Thyroid Nodules (code)	0.8254	1st Place in MICCAI 2020
20201004	Huai Chen	LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images	0.8196	2nd Place in MICCAI 2020
20201004	Zhe Tang	Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation	0.8194	3rd Place in MICCAI 2020

Video records are available on pathable

Endoscopy Computer Vision Challenge (EndoCV2020)

Date	First Author	Title	Avg F1 and F2	Remark
202004	Vajira Thambawita	DivergentNets: Medical Image Segmentation by Network Ensemble (paper) (code)	0.823	1st Place in ISBI EndoCV 2020

2020 ICIAR: Automatic Lung Cancer Patient Management (LNDb) (LNDb)

Results

Date	First Author	Title	IoU	Remark
20200625	Alexandr G. Rassadin	Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung (arxiv)	0.5221	1st Place in Seg. Task

Challenges on Open Leaderboard Phase

2019 MICCAI: Kidney Tumor Segmentation Challenge (KiTS19)

Leaderboard (2019/07/30)

Date	First Author	Title	Composite Dice	Kidney Dice	Tumor Dice
202004	Fabian Isensee	Automated Design of Deep Learning Methods for Biomedical Image Segmentation (arxiv)	0.9168	0.9793	0.8542
20190730	Fabian Isensee	An attempt at beating the 3D U-Net (paper)	0.9123	0.9737	0.8509
20190730	Xiaoshuai Hou	Cascaded Semantic Segmentation for Kidney and Tumor (paper)	0.9064	0.9674	0.8454
20190730	Guangrui Mu	Segmentation of kidney tumor by multi-resolution VB-nets (paper)	0.9025	0.9729	0.8321

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 Per Case Dice	Liver Global Dice	Tumor Per Case Dice	Tumor Global Dice
202004	Fabian Isensee	Automated Design of Deep Learning Methods for Biomedical Image Segmentation (arxiv)	0.967	0.970	0.763	0.858
201909	Xudong Wang	Volumetric Attention for 3D Medical Image Segmentation and Detection (MICCAI2019)	-	-	0.741	-
201908	Jianpeng Zhang	Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation (IJCAI 2019)	0.965	0.968	0.730	0.820
202007	Youbao Tang	E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans (arXiv)	0.966	0.968	0.724	0.829
201709	Xiaomeng Li	H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, (TMI), (Keras code)	0.961	0.965	0.722	0.824

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

Recent results can be found here.

Task	Data Info	Fabian Isensee et al. (paper)	nnUNet v2	Qihang Yu et al. (paper)
Brats	Multimodal multisite MRI data (FLAIR, T1w, T1gd,T2w), (484 Training + 266 Testing)	0.68/0.48/0.68	68/46.8/68.46	67.6/48.6/69.7
Heart	Mono-modal MRI (20 Training + 10 Testing)	0.93	96.74	92.49
Hippocampus head and body	Mono-modal MRI (263 Training + 131 Testing)	0.90/0.89	90/88.69	89.37/87.96
Liver & Tumor	Portal venous phase CT (131 Training + 70 Testing)	0.95/0.74	95.75/75.97	94.98/72.89
Lung	CT (64 Training + 32 Testing)	0.69	73.97	70.44
Pancreas & Tumor	Portal venous phase CT (282 Training +139 Testing)	0.80/0.52	81.64/52.78	80.76/54.41
Prostate central gland and peripheral	Multimodal MR (T2, ADC) (32 Training + 16 Testing)	0.76/0.90	76.59/89.62	74.88/88.75
Hepatic vessel& Tumor	CT, (303 Training + 140 Testing)	0.63/0.69	66.46/71.78	64.73/71
Spleen	CT (41 Training + 20 Testing)	0.96	97.43	96.28
Colon	CT (41 Training + 20 Testing)	0.56	58.33	58.90

Only showing Dice Score.

Recent papers on Medical Segmentation Decathlon

Date	First Author	Title	Score
20181129	Yingda Xia	3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training (paper)	no test set 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)

2020 MICCAI-MyoPS: Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020)

Date	First Author	Title	Scar	Scar+Edema	Remark
20201004	Shuwei Zhai	Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble (paper in press)	0.672 (0.244)	0.731 (0.109)	1st Place in MICCAI 2020

2019 MICCAI: Structure Segmentation for Radiotherapy Planning (StructSeg)

Results

Date	First Author	Title	Head & Neck OAR	Head & Neck GTV	Chest OAR	Chest GTV
20191001	Huai Chen	TBD	0.8109	0.6666	0.9011	0.5406
20191001	Fabian Isensee	nnU-Net	0.7988	0.6398	0.9083	0.5343
20191001	Yujin Hu	TBD	0.7956	0.6245	0.9024	0.5447
20191001	Xuechen Liu	TBD	-	-	0.9066	-

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

Multi-sequence ventricle and myocardium segmentation.

Date	First Author	Title	LV	Myo	RV
20190821	Chen Chen	Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation (arxiv)	0.92	0.83	0.88

2019 Kaggle SIIM-ACR Pneumothorax Segmentation

Date	First Author	Title	Dice
20190905	Aimoldin Anuar	SIIM-ACR Pneumothorax Challenge - 1st place solution (pytorch)	0.8679

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

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
Medical Image Analysis	Many excellent contributors	MONAI: Medical Open Network for AI (code)	pytorch
Segmentation	Christian S. Perone	MedicalTorch	pytorch
Segmentation	Fabian Isensee	nnU-Net (paper) (code)	pytorch
MedImgIO	Fernando Pérez García	TorchIO: tools for loading, augmenting and writing 3D medical images on PyTorch (code)	pytorch
Segmentation	DLinRadiology	MegSeg: a free segmentation tool for radiological images (CT and MRI)	homepage
Segmentation	Adaloglou Nikolaos	A 3D multi-modal medical image segmentation library in PyTorch (code)	pytorch

Segmentation Loss Odyssey (paper & code)](https://github.com/JunMa11/SegLoss)

Contribute

Contributions are most welcome!

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