Abstract #4306

Validation of Deep Learning based Tissue Segmentation for Efficient and Robust Quantitation of Background Parenchymal Enhancement on Breast MRI

Yu-Tzu Kuo¹, Anum S. Kazerouni², Vivian Y. Park³, Wesley Surento^2,4, Suleeporn Sujichantararat², Daniel S. Hippe⁵, Habib Rahbar², and Savannah C. Partridge^1,2

¹Department of Bioengineering, University of Washington, Seattle, WA, United States, ²Department of Radiology, University of Washington, Seattle, WA, United States, ³Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, ⁴Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States, ⁵Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States

Synopsis

Keywords: Segmentation, Segmentation, Breast, Machine Learning/Artificial Intelligence

Motivation: Quantitative measurements, such as background parenchymal enhancement (BPE) on breast dynamic contrast-enhanced (DCE) MRI, show potential as markers for breast cancer risk.

Goal(s): This study compares the efficiency and reproducibility of automated deep learning (DL) vs. semi-automated methods for fibroglandular tissue (FGT) segmentation and quantitation of BPE on DCE-MRI.

Approach: Quality of FGT segmentations generated by fuzzy c-means (semi-automated) and DL (fully automated) algorithms were scored by a radiologist, and the agreement of quantitative BPE measurements across segmentation approaches was assessed.

Results: Deep learning-based FGT segmentation offers more accurate and robust FGT segmentation, enhancing quality and maintaining BPE reproducibility.

Impact: Application of deep learning for segmentation of fibroglandular tissue on breast MRI can improve the robustness and reliability of quantitative imaging biomarkers, with the potential to improve risk stratification and clinical decision-making for high-risk breast cancer screening.

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