Automated deep learning method for whole-breast segmentation in contrast-free quantitative MRI.

ABSTRACT:

Background

To develop a deep learning segmentation method utilizing the nnU-Net architecture for fully automated whole-breast segmentation based on diffusion-weighted imaging (DWI) and synthetic MRI (SyMRI) images.

Methods

A total of 98 patients with 196 breasts were evaluated. All patients underwent 3.0T magnetic resonance (MR) examinations, which incorporated DWI and SyMRI techniques. The ground truth for breast segmentation was established through a manual, slice-by-slice approach performed by two experienced radiologists. The U-Net and nnU-Net deep learning algorithms were employed to segment the whole-breast. Performance was evaluated using various metrics, including the Dice Similarity Coefficient (DSC), accuracy, and Pearson's correlation coefficient.

Results

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SUBMITTER: Gao W 

PROVIDER: S-EPMC12465285 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature