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Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning

Authors :
Chunxia Chen
Liu Xiong
Yongping Lin
Ming Li
Zhiyu Song
Jialin Su
Wenting Cao
Source :
BioMedical Engineering OnLine, Vol 23, Iss 1, Pp 1-18 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

Details

Language :
English
ISSN :
1475925X
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BioMedical Engineering OnLine
Publication Type :
Academic Journal
Accession number :
edsdoj.4f07f8db650c4f4a9dcca2daec1d6e57
Document Type :
article
Full Text :
https://doi.org/10.1186/s12938-024-01281-5