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Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning 期刊论文

编号：

643719AACD1A3622560C9D840241B25C
作者：

Zhang, Jiadong#^[1,2]Cui, Zhiming^[1];Zhou, Luping*^[3]Sun, Yiqun(孙轶群)^[4]Li, Zhenhui(李振辉)*^[5]Liu, Zaiyi(刘再毅)^[6]Shen, Dinggang(沈定刚)*^[1,7,8]
语种：

英文
期刊：

IEEE TRANSACTIONS ON MEDICAL IMAGING ISSN：0278-0062 2023 年 42 卷 12 期 (3944 - 3955) ; DEC
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关键词：

Image segmentation Image reconstruction Breast Training Task analysis Biomedical imaging Magnetic resonance imaging Breast tissue segmentation automated background parenchymal enhancement (BPE) quantification semi-supervised learning
摘要：

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.
推荐引用方式
GB/T 7714：

Zhang Jiadong,Cui Zhiming,Zhou Luping, et al. Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning [J].IEEE TRANSACTIONS ON MEDICAL IMAGING,2023,42(12):3944-3955.
APA：

Zhang Jiadong,Cui Zhiming,Zhou Luping,Sun Yiqun,&Shen Dinggang.(2023).Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning .IEEE TRANSACTIONS ON MEDICAL IMAGING,42(12):3944-3955.
MLA：

Zhang Jiadong, et al. "Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning" .IEEE TRANSACTIONS ON MEDICAL IMAGING 42,12(2023):3944-3955.
入库时间：

2024/10/31 9:59:00
更新时间：

2024/10/31 9:59:00
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