A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

PURPOSE: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

METHODS: We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed.

RESULTS: The average Dice coefficient for the breast parenchyma is [Formula: see text], which outperforms the classical state-of-the-art approach by a margin of [Formula: see text].

CONCLUSION: The proposed solution is accurate and highly efficient and has potential to be applied for big epidemiological data with thousands of participants.