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JunMa11

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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 202010)

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

Ongoing Challenges

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: 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

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)

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