基于DCE-MRI的乳腺病灶分割与良恶性分类方法的研究进展

doi:10.3969/j.issn.0258-8021.2025.06.009

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Abstract Breast cancer is one of the most common malignant tumors threatening women′s health, and early and accurate diagnosis plays an important role in the prognosis of breast cancer patients. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides an important technical support for segmentation of breast lesions and discrimination of benign and malignant by virtue of its high resolution and dynamic flow analysis capability.In this article, the research progress of breast lesion segmentation and classification techniques based on DCE-MRI was reviewed, starting from non-automated traditional image segmentation and machine learning models based on manual feature extraction, moving on to deep learning techniques represented by convolutional neural networks, and further extending to analysis strategies integrating multi-modal imaging data as well as other emerging technologies. The technical characteristics and clinical application value of various methods were elaborated in detail. The current status of breast lesion segmentation and benign-malignant classification technologies was summarized, and future development directions were outlined. Specifically, emphasis should be placed on multi-center data collaboration, model interpretability, and development strategies integrating artificial intelligence with clinical practice, so that a safe and efficient intelligent auxiliary diagnosis system could be established to provide reliable support for the precise diagnosis and treatment of breast cancer.

Key words： breast cancer dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast lesion segmentation benign-malignant classification

Received: 27 April 2025

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R318

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	Articles by authors
	Huang Kaiyang
	Li Xiujuan
	Xie Yuanzhong
	Hou Jixue
	Han Baosan
	Nie Shengdong

Cite this article:

Huang Kaiyang,Li Xiujuan,Xie Yuanzhong, et al. Research Advances in DCE-MRI-Based Breast Lesion Segmentation and Benign-Malignant Classification Methods[J]. Chinese Journal of Biomedical Engineering, 2025, 44(6): 730-737.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.06.009 OR http://cjbme.csbme.org/EN/Y2025/V44/I6/730

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