JunMa11 / Sota Medseg
SOTA medical image segmentation methods based on various challenges
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State-of-the-art medical image segmentation methods based on various challenges! (Updated 202101)
Contents
Head and Neck
- 2020 MICCAI: Retinal Fundus Glaucoma Challenge Edition2 (REFUGE2) (Results)
- 2020 MICCAI: Brain Tumor Segmentation Challenge (BraTS) (Results)
- 2020 MICCAI: CATARACTS Semantic Segmentation
- 2020 MICCAI: Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images (ABCs) (Results)
- 2020 MICCAI: 3D Head and Neck Tumor Segmentation (HECKTOR) (Results)
- 2020 MICCAI: Cerebral Aneurysm Segmentation (CADA) (Results)
- 2020 MICCAI: Aneurysm Detection And segMenation Challenge 2020 (ADAM) (Results)
- 2020 MICCAI: Thyroid nodule segmentation and classification challenge (TN-SCUI 2020). (Results)
- 2020 ICIAR: Automatic Lung Cancer Patient Management (LNDb) (LNDb)
- 2019 MICCAI: Multimodal Brain Tumor Segmentation Challenge (BraTS2019) (Results)
- 2019 MICCAI: 6-month Infant Brain MRI Segmentation from Multiple Sites (iSeg2019) (Results)
- 2019 MICCAI: Automatic Structure Segmentation for Radiotherapy Planning Challenge (Results)
- 2018 MICCAI: Multimodal Brain Tumor Segmentation Challenge
- 2018 MICCAI: Ischemic stroke lesion segmentation
- 2018 MICCAI Grand Challenge on MR Brain Image Segmentation
Chest & Abdomen
- 2020 MICCAI: Large Scale Vertebrae Segmentation Challenge (VerSe) (Results)
- 2020 MICCAI: Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI (EMIDEC)
- 2020 MICCAI: Automated Segmentation of Coronary Arteries (ASOCA) (Results)
- 2020 MICCAI: MyoPS 2020: Myocardial pathology segmentation combining multi-sequence CMR (Homepage)
- 2019 MICCAI: VerSe2019: Large Scale Vertebrae Segmentation Challenge (Results)
- 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 (MSD) (Results)
- 2020 MICCAI: Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ) (Results)
- Awesome Open Source Tools
- Loss Odyssey in Medical Image Segmentation
Survey
@article{Ma-SOTASeg2020,
title={Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?},
author={Jun Ma},
journal={arXiv preprint arXiv:2101.00232},
year={2021}
}
Ongoing Challenges
(CADA) (Results)
2020 MICCAI: Cerebral Aneurysm SegmentationDate | 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 |
(EMIDEC)
2020 MICCAI: Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRIDate | 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
(ADAM) (Results)
Aneurysm Detection And segMenation Challenge 2020Date | 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 |
(M&Ms) (Results)
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation ChallengeDate | 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
(HECKTOR 2020). (Results)
2020 MICCAI: 3D Head and Neck Tumor Segmentation in PET/CTDate | 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 |
(TN-SCUI 2020). (Results)
2020 MICCAI: Thyroid nodule segmentation and classification challengeDate | 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
(LNDb)
2020 ICIAR: Automatic Lung Cancer Patient Management (LNDb)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
(KiTS19)
2019 MICCAI: Kidney Tumor Segmentation ChallengeDate | 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 |
(LiTS)
2017 ISBI & MICCAI: Liver tumor segmentation challengeSummary: 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 |
(PROMISE12)
2012 MICCAI: Prostate MR Image SegmentationDate | 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)
(MyoPS 2020)
2020 MICCAI-MyoPS: Myocardial pathology segmentation combining multi-sequence CMRDate | 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 |
(StructSeg)
2019 MICCAI: Structure Segmentation for Radiotherapy PlanningDate | 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 | - |
(MS-CMRSeg)
2019 MICCAI: Multi-sequence Cardiac MR Segmentation ChallengeMulti-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 |
(SegTHOR)
2019 ISBI: Segmentation of THoracic Organs at Risk in CT imagesDate | 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 |
(BraTS)
2018 MICCAI: Multimodal Brain Tumor Segmentation ChallengeSummary: 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 | - |
(ISLES )
2018 MICCAI: Ischemic stroke lesion segmentationDate | 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) | - |
(MRBrainS18)
2018 MICCAI Grand Challenge on MR Brain Image Segmentation- 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 |
(LVQuan18)
2018 MICCAI: Left Ventricle Full Quantification ChallengeRank | 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) |
(AtriaSeg)
2018 MICCAI: Atrial Segmentation ChallengeRank | 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 |
https://github.com/JunMa11/SegLoss)
Segmentation Loss Odyssey (paper & code)](Contribute
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