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Recently, semi-supervised image segmentation has become a hot topic in medical image computing, unfortunately, there are only a few open-source codes and datasets, since the privacy policy and others. For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning research in the medical image computing community. If you are interested, you can push your implementations or ideas to this repo or contact us at any time.
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This repo has re-implemented these semi-supervised methods (with some modifications for semi-supervised medical image segmentation, more details please refer to these original works): (1) Mean Teacher; (2) Entropy Minimization; (3) Deep Adversarial Networks; (4) Uncertainty Aware Mean Teacher; (5) Interpolation Consistency Training; (6) Uncertainty Rectified Pyramid Consistency; (7) Cross Pseudo Supervision; (8) Cross Consistency Training; (9) Deep Co-Training. In addition, several backbones networks (both 2D and 3D) are also supported in this repo, such as UNet, VNet, AttentionUNet, ENet, etc.
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This project was originally developed for our previous works URPC (MICCAI2021 early accept). If you use this codebase in your research, please cite the following works:
@InProceedings{luo2021urpc, author={Luo, Xiangde and Liao, Wenjun and Chen, Jieneng and Song, Tao and Chen, Yinan and Zhang, Shichuan and Chen, Nianyong and Wang, Guotai and Zhang, Shaoting}, title={Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021}, year={2021}, pages={318--329}} @InProceedings{luo2021semi, title={Semi-supervised Medical Image Segmentation through Dual-task Consistency}, author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai}, journal={AAAI Conference on Artificial Intelligence}, year={2021}, pages={8801-8809}} @misc{ssl4mis2020, title={{SSL4MIS}}, author={Luo, Xiangde}, howpublished={\url{https://github.com/HiLab-git/SSL4MIS}}, year={2020}}
| Date | The First and Last Authors | Title | Code | Reference |
|---|---|---|---|---|
| 2021-12 | C. Seibold and R. Stiefelhagen | Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation | None | AAAI2022 |
| 2021-11 | X. Zheng and C. Sham | Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2021-11 | J. Peng and M. Pedersoli | Diversified Multi-prototype Representation for Semi-supervised Segmentation | Code | Arxiv |
| 2021-10 | J. Hou and J. Deng | Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations | None | WACV2021 |
| 2021-10 | M. Xu and J. Jacob | MisMatch: Learning to Change Predictive Confidences with Attention for Consistency-Based, Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-10 | H. Wu and J. Qin | Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-supervised Polyp Segmentation | None | ICCV2021 |
| 2021-10 | Y. Shi and Y. Gao | Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation | Code | TMI2021 |
| 2021-09 | Z. Xu and R. Tong | All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2021-09 | G. Wang and S. Zhang | Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention | Code | TMI2021 |
| 2021-09 | K. Wang and Y. Wang | Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | H. Huang and R. Tong | 3D Graph-S2Net: Shape-Aware Self-ensembling Network for Semi-supervised Segmentation with Bilateral Graph Convolution | None | MICCAI2021 |
| 2021-09 | L. Zhu and B. Ooi | Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | R. Zhang and G. Li | Self-supervised Correction Learning for Semi-supervised Biomedical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | D. Kiyasseh and A. Chen | Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning | None | MICCAI2021 |
| 2021-09 | Y. Wu and J. Cai | Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation | None | Arxiv |
| 2021-09 | X. Zeng and Y. Wang | Reciprocal Learning for Semi-supervised Segmentation | Code | MICCAI2021 |
| 2021-09 | G. Zhang and S. Jiang | Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net | None | CMPB2021 |
| 2021-09 | J. Chen and G. Yang | Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data | Code | TMI2021 |
| 2021-09 | X. Hu and Y. Shi | Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation | Code | MICCAI2021 |
| 2021-09 | G. Chen and J. Shi | MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation | Code | NeuroImage2021 |
| 2021-08 | H. Peiris and M. Harandi | Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation | Code | MICCAI2021 |
| 2021-08 | J. Sun and Y. Kong | Semi-Supervised Medical Image Semantic Segmentation with Multi-scale Graph Cut Loss | None | ICIP2021 |
| 2021-08 | X. Shen and J. Lu | PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning | None | ArXiv |
| 2021-08 | C. You and J. Duncan | SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-08 | C. Li and P. Heng | Self-Ensembling Co-Training Framework for Semi-supervised COVID-19 CT Segmentation | None | JBHI2021 |
| 2021-08 | H. Yang and P. H. N. With | Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning | None | JBHI2021 |
| 2021-07 | Q. Xu and X. Wang | Semi-supervised Medical Image Segmentation with Confidence Calibration | None | IJCNN |
| 2021-07 | W. Ding and H. Hawash | RCTE: A Reliable and Consistent Temporal-ensembling Framework for Semi-supervised Segmentation of COVID-19 Lesions | None | Information Fusion2021 |
| 2021-06 | X. Liu and S. A. Tsaftaris | Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation | Code | MICCAI2021 |
| 2021-06 | P. Pandey and Mausam | Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation | None | MICCAI2021 |
| 2021-06 | C. Li and Y. Yu | Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation | None | Arxiv |
| 2021-05 | J. Xiang and S. Zhang | Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2021-05 | S. Li and C. Guan | Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation | None | Arxiv |
| 2021-05 | C. You and J. Duncan | Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation | None | Arxiv |
| 2021-05 | Z. Xie and J. Yang | Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence | None | ICASSP2021 |
| 2021-04 | S. Reiß and R. Stiefelhagen | Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation | None | CVPR2021 |
| 2021-04 | S. Chatterjee and A. Nurnberger | DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data | Code | MIDL |
| 2021-04 | A. Meyer and M. Rak | Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond | Code | Arxiv |
| 2021-04 | Y. Li and P. Heng | Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images | None | MICCAI2021 |
| 2021-03 | Y. Zhang and C. Zhang | Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation | Code | PRCV2021 |
| 2021-03 | J. Peng and C. Desrosiers | Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization | Code | MELBA |
| 2021-03 | Y. Wu and L. Zhang | Semi-supervised Left Atrium Segmentation with Mutual Consistency Training | None | MICCAI2021 |
| 2021-02 | J. Peng and Y. Wang | Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models | None | Arxiv |
| 2021-02 | J. Dolz and I. B. Ayed | Teach me to segment with mixed supervision: Confident students become masters | Code | IPMI2021 |
| 2021-02 | C. Cabrera and K. McGuinness | Semi-supervised Segmentation of Cardiac MRI using Image Registration | None | Under review for MIDL2021 |
| 2021-02 | Y. Wang and A. Yuille | Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction | None | TMI2021 |
| 2021-02 | R. Alizadehsaniand U R. Acharya | Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data | None | Arxiv |
| 2021-02 | D. Yang and D. Xu | Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan | None | MedIA2021 |
| 2020-01 | E. Takaya and S. Kurihara | Sequential Semi-supervised Segmentation for Serial Electron Microscopy Image with Small Number of Labels | Code | Journal of Neuroscience Methods |
| 2021-01 | Y. Zhang and Z. He | Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer | None | Arxiv |
| 2020-12 | H. Wang and D. Chen | Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation | None | Arxiv |
| 2020-12 | X. Luo and S. Zhang | Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency | Code | MICCAI2021 |
| 2020-12 | M. Abdel‐Basset and M. Ryan | FSS-2019-nCov: A Deep Learning Architecture for Semi-supervised Few-Shot Segmentation of COVID-19 Infection | None | Knowledge-Based Systems2020 |
| 2020-11 | N. Horlava and N. Scherf | A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data | None | Arxiv |
| 2020-11 | P. Wang and C. Desrosiers | Self-paced and self-consistent co-training for semi-supervised image segmentation | None | MedIA2021 |
| 2020-10 | Y. Sun and L. Wang | Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation | None | MLMI2020 |
| 2020-10 | L. Chen and D. Merhof | Semi-supervised Instance Segmentation with a Learned Shape Prior | Code | LABELS2020 |
| 2020-10 | S. Shailja and B.S. Manjunath | Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy | Code | Arxiv |
| 2020-10 | L. Sun and Y. Yu | A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision | None | Arxiv |
| 2020-10 | J. Ma and X. Yang | Active Contour Regularized Semi-supervised Learning for COVID-19 CT Infection Segmentation with Limited Annotations | Code | Physics in Medicine & Biology2020 |
| 2020-10 | W. Hang and J. Qin | Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation | Code | MICCAI2020 |
| 2020-10 | K. Tan and J. Duncan | A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography | None | MICCAI2020 |
| 2020-10 | Y. Wang and Z. He | Double-Uncertainty Weighted Method for Semi-supervised Learning | None | MICCAI2020 |
| 2020-10 | K. Fang and W. Li | DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images | None | MICCAI2020 |
| 2020-10 | X. Cao and L. Cheng | Uncertainty Aware Temporal-Ensembling Model for Semi-supervised ABUS Mass Segmentation | None | TMI2020 |
| 2020-09 | Z. Zhang and W. Zhang | Semi-supervised Semantic Segmentation of Organs at Risk on 3D Pelvic CT Images | None | Arxiv |
| 2020-09 | J. Wang and G. Xie | Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions | None | BMVC2020 |
| 2020-09 | X. Luo and S. Zhang | Semi-supervised Medical Image Segmentation through Dual-task Consistency | Code | AAAI2021 |
| 2020-08 | X. Huo and Q. Tian | ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation | None | Arxiv |
| 2020-08 | Y. Xie and Y. Xia | Pairwise Relation Learning for Semi-supervised Gland Segmentation | None | MICCAI2020 |
| 2020-07 | K. Chaitanya and E. Konukoglu | Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation | Code | Arxiv |
| 2020-07 | S. Li and X. He | Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images | Code | MICCAI2020 |
| 2020-07 | Y. Li and Y. Zheng | Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation | None | MICCAI2020 |
| 2020-07 | Z. Zhao and P. Heng | Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video | Code | MICCAI2020 |
| 2020-07 | Y. Zhou and P. Heng | Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation | Code | MICCAI2020 |
| 2020-07 | A. Tehrani and H. Rivaz | Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography | None | MICCAI2020 |
| 2020-07 | Y. He and S. Li | Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation | None | MedIA2020 |
| 2020-07 | J. Peng and C. Desrosiers | Mutual information deep regularization for semi-supervised segmentation | Code | MIDL2020 |
| 2020-07 | Y. Xia and H. Roth | Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation | None | WACV2020,MedIA2020 |
| 2020-07 | X. Li and P. Heng | Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation | Code | TNNLS2020 |
| 2020-06 | F. Garcıa and S. Ourselin | Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning | None | MICCAI2020 |
| 2020-06 | H. Yang and P. With | Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet | None | MICCAI2020 |
| 2020-05 | G. Fotedar and X. Ding | Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts | None | MICCAI2020 |
| 2020-04 | C. Liu and C. Ye | Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions | None | ISBI2020 |
| 2020-04 | R. Li and D. Auer | A Generic Ensemble Based Deep Convolutional Neural Network for Semi-Supervised Medical Image Segmentation | Code | ISBI2020 |
| 2020-04 | K. Ta and J. Duncan | A Semi-Supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography | None | ISBI2020 |
| 2020-04 | Q. Chang and D. Metaxas | Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI | None | ISBI2020 |
| 2020-04 | D. Fan and L. Shao | Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images | Code | TMI2020 |
| 2019-10 | L. Yu and P. Heng | Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation | Code | MICCAI2019 |
| 2019-10 | G. Bortsova and M. Bruijne | Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations | None | MICCAI2019 |
| 2019-10 | Y. He and S. Li | DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy | None | MICCAI2019 |
| 2019-10 | H. Zheng and X. Han | Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior | None | MICCAI2019 |
| 2019-10 | P. Ganayea and H. Cattin | Removing Segmentation Inconsistencies with Semi-Supervised Non-Adjacency Constraint | Code | MedIA2019 |
| 2019-10 | Y. Zhao and C. Liu | Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network | None | MICCAI2019 |
| 2019-10 | H. Kervade and I. Ayed | Curriculum semi-supervised segmentation | None | MICCAI2019 |
| 2019-10 | S. Chen and M. Bruijne | Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation | None | MICCAI2019 |
| 2019-10 | Z. Xu and M. Niethammer | DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation | None | MICCAI2019 |
| 2019-10 | S. Sedai and R. Garnavi | Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images | None | MICCAI2019 |
| 2019-10 | G. Pombo and P. Nachev | Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning | Code | MICCAI2019 |
| 2019-06 | W. Cui and C. Ye | Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model | None | IPMI2019 |
| 2019-06 | K. Chaitanya and E. Konukoglu | Semi-supervised and Task-Driven Data Augmentation | Code | IPMI2019 |
| 2019-04 | M. Jafari and P. Abolmaesumi | Semi-Supervised Learning For Cardiac Left Ventricle Segmentation Using Conditional Deep Generative Models as Prior | None | ISBI2019 |
| 2019-03 | Z. Zhao and Z. Zeng | Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation | None | BHI |
| 2019-03 | J. Peng and C. Desrosiers | Deep co-training for semi-supervised image segmentation | Code | PR2020 |
| 2019-01 | Y. Zhou and A. Yuille | Semi-Supervised 3D Abdominal Multi-Organ Segmentation via Deep Multi-Planar Co-Training | None | WACV2019 |
| 2018-10 | P. Ganaye and H. Cattin | Semi-supervised Learning for Segmentation Under Semantic Constraint | Code | MICCAI2018 |
| 2018-10 | A. Chartsias and S. Tsaftari | Factorised spatial representation learning: application in semi-supervised myocardial segmentation | None | MICCAI2018 |
| 2018-09 | X. Li and P. Heng | Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model | Code | BMVC2018 |
| 2018-04 | Z. Feng and D. Shen | Semi-supervised learning for pelvic MR image segmentation based on multi-task residual fully convolutional networks | None | ISBI2018 |
| 2017-09 | L. Gu and S. Aiso | Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels) | None | MICCAI2017 |
| 2017-09 | S. Sedai and R. Garnavi | Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder | None | MICCAI2017 |
| 2017-09 | W. Bai and D. Rueckert | Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation | None | MICCAI2017 |
Some implementations of semi-supervised learning methods can be found in this Link.
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This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algorithms' implementations.
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In the next two or three months, we will provide more algorithms' implementations, examples, and pre-trained models.
- If you have any questions or suggestions about this project, please contact me through email:
[email protected]or QQ Group (Chinese):906808850.
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