Cross-Parametric Generative Adversarial Network-Based Magnetic Resonance Image Feature Synthesis for Breast Lesion Classification

计算机科学 人工智能 判别式 特征(语言学) 磁共振成像 模式识别(心理学) 乳房磁振造影 参数统计 基本事实 特征提取 计算机视觉 乳腺癌 乳腺摄影术 癌症 放射科 医学 数学 哲学 内科学 统计 语言学
作者
Ming Fan,Guangyao Huang,Junhong Lou,Xin Gao,Tieyong Zeng,Lihua Li
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:27 (11): 5495-5505 被引量:5
标识
DOI:10.1109/jbhi.2023.3311021
摘要

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains information on tumor morphology and physiology for breast cancer diagnosis and treatment. However, this technology requires contrast agent injection with more acquisition time than other parametric images, such as T2-weighted imaging (T2WI). Current image synthesis methods attempt to map the image data from one domain to another, whereas it is challenging or even infeasible to map the images with one sequence into images with multiple sequences. Here, we propose a new approach of cross-parametric generative adversarial network (GAN)-based feature synthesis (CPGANFS) to generate discriminative DCE-MRI features from T2WI with applications in breast cancer diagnosis. The proposed approach decodes the T2W images into latent cross-parameter features to reconstruct the DCE-MRI and T2WI features by balancing the information shared between the two. A Wasserstein GAN with a gradient penalty is employed to differentiate the T2WI-generated features from ground-truth features extracted from DCE-MRI. The synthesized DCE-MRI feature-based model achieved significantly (p = 0.036) higher prediction performance (AUC = 0.866) in breast cancer diagnosis than that based on T2WI (AUC = 0.815). Visualization of the model shows that our CPGANFS method enhances the predictive power by levitating attention to the lesion and the surrounding parenchyma areas, which is driven by the interparametric information learned from T2WI and DCE-MRI. Our proposed CPGANFS provides a framework for cross-parametric MR image feature generation from a single-sequence image guided by an information-rich, time-series image with kinetic information. Extensive experimental results demonstrate its effectiveness with high interpretability and improved performance in breast cancer diagnosis.
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