Abstract #0600

Deep learning-based whole breast segmentation to support automated breast density measurements from fat-water decomposition MRI

Karl Spuhler¹, Jie Ding², Maria Altbach³, Jean‐Philippe Galons³, Patricia Thompson⁴, Alison Stopeck⁵, and Chuan Huang^1,6,7

¹Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Biomedical Engingeering, Stony Brook University, Stony Brook, NY, United States, ³Medical Imaging, University of Arizona College of Medicine, Tucson, AZ, United States, ⁴Pathology, Stony Brook Medicine, Stony Brook, NY, United States, ⁵Hematology & Oncology, Stony Brook Medicine, Stony Brook, NY, United States, ⁶Radiology, Stony Brook Medicine, Stony Brook, NY, United States, ⁷Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States

Breast density monitoring has become a clinically interesting topic in the past several years. MRI-based methods are attractive because they allow for frequent monitoring without ionizing radiation. Here, we present evidence that a convolutional neural network can replace manual or algorithmic breast segmentation in such pipelines.

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