Semi‐supervised cine cardiac MRI segmentation via joint registration and temporal attention perceiver

The proposed approach outperformed several commonly used semi-supervised segmentation methods and yielded segmentation accuracies on par with fully supervised Unet using various relatively small datasets and under annotations for training.

Authors

Fumin Guo (ORCID: https://orcid.org/0000-0001-6220-1455)
Sa Xiao (ORCID: https://orcid.org/0000-0002-4574-1406)
Xin Zhou (ORCID: https://orcid.org/0000-0003-2814-3399)
Lei Zhang (ORCID: https://orcid.org/0000-0002-2839-8693)
Y. Qin
Ziyin Wang (ORCID: https://orcid.org/0000-0002-8366-2725)

Institutions

Wuhan Institute of Physics and Mathematics (CN)
Wuhan National Laboratory for Optoelectronics (CN)
Huazhong University of Science and Technology (CN)

Publication Details

Journal: Medical Physics
Published: 2025-10-27
DOI: https://doi.org/10.1002/mp.70094
Primary Topic: Medical Image Segmentation Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Semi‐supervised cine cardiac MRI segmentation via joint registration and temporal attention perceiver

Fumin Guo, Sa Xiao, Xin Zhou, Lei Zhang et al.

Oct 27, 2025

Medical Physics

Medical Image Segmentation Techniques

article

Semi‐supervised cine cardiac MRI segmentation via joint registration and temporal attention perceiver

Fumin Guo, Sa Xiao, Xin Zhou, Lei Zhang, Y. Qin, Ziyin Wang

October 27, 2025article en

Abstract

The proposed approach outperformed several commonly used semi-supervised segmentation methods and yielded segmentation accuracies on par with fully supervised Unet using various relatively small datasets and under annotations for training.

Medical PhysicsVol. 52(11)

Wuhan Institute of Physics and Mathematics (CN), Wuhan National Laboratory for Optoelectronics (CN), Huazhong University of Science and Technology (CN)

Openalex Percentile: Top 69%

Medical Image Segmentation Techniques

DOI Source OpenAlex

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