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
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
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 |
| 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 |
| 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 |
Contribute
Contributions are most welcome!
⬆ back to top
