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State-of-the-art medical image segmentation methods based on various challenges! (Updated 201908)

Contents

Head

  • 2019 MICCAI: Multimodal Brain Tumor Segmentation Challenge (BraTS2019) (Ongoing!!!)
  • 2019 MICCAI: Automatic Structure Segmentation for Radiotherapy Planning Challenge (Ongoing!!!)
  • 2018 MICCAI: Multimodal Brain Tumor Segmentation Challenge
  • 2018 MICCAI: Ischemic stroke lesion segmentation
  • 2018 MICCAI Grand Challenge on MR Brain Image Segmentation

Chest & Abdomen

  • 2019 MICCAI: VerSe2019: Large Scale Vertebrae Segmentation Challenge (Ongoing!!!)
  • 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
  • Awesome Open Source Tools
  • Loss functions for class imbalanced Problems

Brain

  • 2019 MICCAI: Multimodal Brain Tumor Segmentation Challenge (BraTS2019) (Ongoing!!!)
  • 2019 MICCAI: 6-month Infant Brain MRI Segmentation from Multiple Sites (iSeg2019) (Ongoing!!!)

Heart

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

Waiting for results

Multi-sequence ventricle and myocardium segmentation.

Chest and Abdomen

2019 MICCAI: Kidney Tumor Segmentation Challenge (KiTS19)

Leaderboard (2019/07/30)

Date First Author Title Composite Dice Kidney Dice Tumor Dice Remark
20190730 Fabian Isensee An attempt at beating the 3D U-Net (paper) 0.9123 0.9737 0.8509 1st Place
20190730 Xiaoshuai Hou Cascaded Semantic Segmentation for Kidney and Tumor (paper) 0.9064 0.9674 0.8454 2nd Place
20190730 Guangrui Mu Segmentation of kidney tumor by multi-resolution VB-nets (paper) 0.9025 0.9729 0.8321 3rd Place

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

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 Dice Tumor Dice
201709 Xiaomeng Li H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, (paper), (Keras code) 0.961 0.722

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

Task Data Info Fabian Isensee et al. (paper) Yingda Xia et al. (paper)
Brats Multimodal multisite MRI data (FLAIR, T1w, T1gd,T2w), (484 Training + 266 Testing) 0.68/0.48/0.68 0.675/0.45/0.68
Heart Mono-modal MRI (20 Training + 10 Testing) 0.93 0.925
Hippocampus head and body Mono-modal MRI (263 Training + 131 Testing) 0.90/0.89 0.88/0.867
Liver & Tumor Portal venous phase CT (131 Training + 70 Testing) 0.95/0.74 0.95/0.714
Lung CT (64 Training + 32 Testing) 0.69 0.52
Pancreas & Tumor Portal venous phase CT (282 Training +139 Testing) 0.80/0.52 0.784/0.385
Prostate central gland and peripheral Multimodal MR (T2, ADC) (32 Training + 16 Testing) 0.76/0.90 0.69/0.867
Hepatic vessel& Tumor CT, (303 Training + 140 Testing) 0.63/0.69 -
Spleen CT (41 Training + 20 Testing) 0.96 -
Colon CT (41 Training + 20 Testing) 0.56 -

Only showing Dice Score.

Recent papers on Medical Segmentation Decathlon

Date First Author Title 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)

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 0.4
Medical Image Analysis Eli Gibson and Wenqi Li NiftyNet: a deep-learning platform for medical imaging (paper), (code) Tensorflow 1.12
Segmentation Christian S. Perone MedicalTorch pytorch>=0.4
awesome-semantic-segmentation mrgloom awesome-semantic-segmentation 3000+ stars
Segmentation Fabian Isensee nnU-Net (paper) (code) 100+stars

Loss functions for Imbalanced Problems

Date First Author Title Conference/Journal
201904 Davood Karimi Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks (paper) arxiv
201901 Seyed Raein Hashemi Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection (paper) IEEE Access
201812 Hoel Kervadec Boundary loss for highly unbalanced segmentation (paper), (pytorch 1.0 code) MIDL 2019
201810 Nabila Abraham A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation (paper) ISBI 2019
201809 Fabian Isensee CE+Dice nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (paper) arxiv
201808 Ken C. L. Wong 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes (paper) MICCAI 2018
201806 Javier Ribera Weighted Hausdorff Distance: Locating Objects Without Bounding Boxes (paper), (pytorch code) CVPR 2019
201708 Tsung-Yi Lin Focal Loss for Dense Object Detection (paper), (code) ICCV, TPAMI
20170711 Carole Sudre Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (paper) DLMIA 2017
20170703 Lucas Fidon Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks (paper) MICCAI 2017 BrainLes
201705 Maxim Berman The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks (paper), (code) CVPR 2018
201701 Seyed Sadegh Mohseni Salehi Tversky loss function for image segmentation using 3D fully convolutional deep networks (paper) MICCAI 2017 MLMI
201612 Md Atiqur Rahman Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation (paper) 2016 International Symposium on Visual Computing
201606 Fausto Milletari "Dice Loss" V-net: Fully convolutional neural networks for volumetric medical image segmentation (paper), (caffe code) International Conference on 3D Vision
201511 Tom Brosch "Sensitivity-Specifity loss" Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation (paper) MICCAI 2015
201505 Olaf Ronneberger "Weighted cross entropy" U-Net: Convolutional Networks for Biomedical Image Segmentation (paper) MICCAI 2015
201309 Gabriela Csurka What is a good evaluation measure for semantic segmentation? (paper) BMVA 2013

Most of the corresponding code can be found here.

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