Dang H, Li M, Tao X, Zhang G, Qi X. Lvsegnet: a novel deep learning-based framework for left ventricle automatic segmentation using magnetic resonance imaging. Comput Commun. 2023;208:124–35.

Article  Google Scholar 

Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imag. 2018;37(11):2514–25.

Article  Google Scholar 

Yu L, Wang S, Li X, Fu C-W, Heng P-A. Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation. In: Medical image computing and computer assisted intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22, 2019;pp. 605–613. Springer

Soret M, Bacharach SL, Buvat I. Partial-volume effect in pet tumor imaging. J Nuclear Med. 2007;48(6):932–45.

Article  Google Scholar 

Wang T, Xu X, Xiong J, Jia Q, Yuan H, Huang M, Zhuang J, Shi Y (2020) Ica-unet: Ica inspired statistical unet for real-time 3d cardiac cine mri segmentation. In: Medical image computing and computer assisted intervention–MICCAI 2020: 23rd international conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23, pp. 447–457. Springer

Albishri AA, Shah SJH, Kang SS, Lee Y. Am-unet: automated mini 3d end-to-end u-net based network for brain claustrum segmentation. Multimedia Tools Appl. 2022;81(25):36171–94.

Article  Google Scholar 

Fang L, Wang X. Multi-input unet model based on the integrated block and the aggregation connection for mri brain tumor segmentation. Biomed Signal Process Control. 2023;79: 104027.

Article  Google Scholar 

Chen X, Yuan Y, Zeng G, Wang J. Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021;pp. 2613–2622

Xiao Z, Sun H, Liu F. Semi-supervised ct image segmentation via contrastive learning based on entropy constraints. Biomed Eng Lett. 2024;14(5):1023–35.

Article  Google Scholar 

Su J, Luo Z, Lian S, Lin D, Li S. Mutual learning with reliable pseudo label for semi-supervised medical image segmentation. Med Image Anal. 2024;94: 103111.

Article  Google Scholar 

Litjens G, Toth R, Van De Ven W, Hoeks C, Kerkstra S, Van Ginneken B, Vincent G, Guillard G, Birbeck N, Zhang J, et al. Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Med Image Anal. 2014;18(2):359–73.

Article  Google Scholar 

Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, Vesal S, Ravikumar N, Maier A, Yang X, et al. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med Image Anal. 2021;67: 101832.

Article  Google Scholar 

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 2015;pp. 234–241. Springer

Zhou L, Liu Y, Zhang Y, Lin Z. Bua-net: boundary and uncertainty-aware attention network for lumbar multi-region magnetic resonance imaging segmentation. Biomed Signal Process Control. 2024;94: 106267.

Article  Google Scholar 

Tejashwini P, Thriveni J, Venugopal K. A novel slca-unet architecture for automatic mri brain tumor segmentation. Biomed Signal Process Control. 2025;100: 107047.

Article  Google Scholar 

Yan Y, Liu R, Chen H, Zhang L, Zhang Q. Cct-unet: a u-shaped network based on convolution coupled transformer for segmentation of peripheral and transition zones in prostate mri. IEEE J Biomed Health Inf. 2023;27(9):4341–51.

Article  Google Scholar 

Wang Y, Wang H, Shen Y, Fei J, Li W, Jin G, Wu L, Zhao R, Le X Semi-supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022;pp. 4248–4257

Luo X, Liao W, Chen J, Song T, Chen Y, Zhang S, Chen N, Wang G, Zhang S. Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: Medical image computing and computer assisted intervention–MICCAI 2021: 24th international conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24, 2021;pp. 318–329. Springer

Yang L, Qi L, Feng L, Zhang W, Shi Y (2023) Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7236–7246

Bai Y, Chen D, Li Q, Shen W, Wang Y. Bidirectional copy-paste for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023;pp. 11514–11524

Chi H, Pang J, Zhang B, Liu W. Adaptive bidirectional displacement for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024;pp. 4070–4080

Tarvainen A, Valpola H. Weight-averaged consistency targets improve semi-supervised deep learning results. In: Proc. of the NIPS 2016;.

Wu Y, Wu Z, Wu Q, Ge Z, Cai J. Exploring smoothness and class-separation for semi-supervised medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, 2022;pp. 34–43. Springer

Wu Y, Xu M, Ge Z, Cai J, Zhang L. Semi-supervised left atrium segmentation with mutual consistency training. In: Medical image computing and computer assisted intervention–MICCAI 2021: 24th international conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24, 2021;pp. 297–306. Springer

Luo X, Chen J, Song T, Wang G. Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI conference on artificial intelligence, 2021;vol. 35, pp. 8801–8809