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 |
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