Semi-supervised-learning-for-medical-image-segmentation.

• Recently, semi-supervised image segmentation has become a hot topic in medical image computing, unfortunately, there are only 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 repository at any time.

Literature reviews of semi-supervised learning approach for medical image segmentation (SSL4MIS).

Date	The First and Last Authors	Title	Code	Reference
2020-11	P. Wang and C. Desrosiers	Self-paced and self-consistent co-training for semi-supervised image segmentation	None	Arxiv
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	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	Arxiv
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	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
2020-04	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. Ducan	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	H. Zheng and X. Han	Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior	None	MICCAI2019
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	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	None	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

Code for semi-supervised medical image segmentation.

Some implementations of semi-supervised learning methods can be found in this Link.

Conclusion

• This repository proivdes a daily-update literature reviews, algorithoms' 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 algorithoms' implementations. It was originally developped for our previous work DTC, if you find it's useful for your research, please consider to cite the following paper:

    @article{luo2020semi,
      title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
      author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Chen, Yinan and Wang, Guotai and Zhang, Shaoting},
      journal={arXiv preprint arXiv:2009.04448},
      year={2020}
    }
  

• In the next two or three months, we will provide more algorithms' implementations, examples, and pre-trained models.

Questions and Suggestions

• If you have any questions or suggestions about this project, please contact me through email: [email protected] or QQ Group (Chinese):906808850.