Multiparametric magnetic resonance imaging (mpMRI) is a key tool for
assessing breast cancer progression. Although deep learning has been applied to
automate tumor segmentation in breast MRI, the effect of sequence combinations
in mpMRI remains under-investigated. This study explores the impact of
different combinations of T2-weighted (T2w), dynamic contrast-enhanced MRI
(DCE-MRI) and diffusion-weighted imaging (DWI) with apparent diffusion
coefficient (ADC) map on breast tumor segmentation using nnU-Net. Evaluated on
a multicenter mpMRI dataset, the nnU-Net model using DCE sequences achieved a
Dice similarity coefficient (DSC) of 0.69 $\pm$ 0.18 for functional tumor
volume (FTV) segmentation. For whole tumor mask (WTM) segmentation, adding the
predicted FTV to DWI and ADC map improved the DSC from 0.57 $\pm$ 0.24 to 0.60
$\pm$ 0.21. Adding T2w did not yield significant improvement, which still
requires further investigation under a more standardized imaging protocol. This
study serves as a foundation for future work on predicting breast cancer
treatment response using mpMRI.

使用 nnU-Net 模型，研究探讨不同序列的 mpMRI 在乳腺肿瘤分割中的影响，并通过在多中心 mpMRI 数据集上的评估，证明了使用动态对比增强 MRI 序列可以获得较高的功能性肿瘤体积分割 Dice 相似系数和整个肿瘤掩膜分割改善。然而，添加 T2w 序列对结果影响不显著，需要进一步研究。该研究为未来的乳腺癌治疗反应预测提供基础。