Unspecified Vinayak Ahluwalia Profile

Vinayak Ahluwalia

at Univ of Pennsylvania

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Publications (1)

Proceedings Article | 29 May 2024 Paper

Advancing volumetric breast density segmentation: a deep learning approach with digital breast tomosynthesis

Nehal Doiphode, Vinayak Ahluwalia, Walter Mankowski, Eric Cohen, Sarthak Pati, Lauren Pantalone, Spyridon Bakas, Ari Brooks, Celine Vachon, Emily Conant, Aimilia Gastounioti, Despina Kontos

Proceedings Volume 13174, 131740O (2024) https://doi.org/10.1117/12.3027010

KEYWORDS: Image segmentation, Breast, Digital breast tomosynthesis, Breast cancer, 3D modeling, Breast density, 3D image processing

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Recognizing breast density as a critical risk factor for breast cancer, traditionally assessed through subjective radiological evaluation within the BI-RADS framework, this research seeks to mitigate inter-observer variability through automated, quantitative analysis. The transition to DBT offers a quasi-3D perspective potentially enhancing the accuracy of BD assessments yet faces limitations with current FDA-cleared methods for volumetric breast density (VBD) estimation. Addressing these challenges, our work introduces a fully automated computational tool leveraging deep learning to accurately assess VBD from 3D DBT images without reliance on raw 2D data. Employing retrospective data compliant with privacy regulations, this study utilized DBT screening examinations from the Hospital of the University of Pennsylvania. The development of a three-class segmentation model, based on the U-Net architecture, was undertaken to differentiate between non-breast/background, fatty breast tissue, and dense breast tissue in DBT images. A novel two-stage training method was devised to enhance model performance, particularly in avoiding mis-segmentation issues common in high-resolution medio-lateral oblique images. This approach first utilized resized images for global shape information recognition, followed by refined segmentation using a 3D U-Net on filtered input, emphasizing accurate dense tissue identification. Our model demonstrated exemplary performance, with the Dice score—a critical metric for evaluating segmentation accuracy—revealing substantial agreement between the model's predictions and actual data. Validation of the model's effectiveness in breast cancer risk estimation was conducted through a case-control study, demonstrating a statistically significant association between DL-estimated VBD and cancer diagnosis. Additional factors, including BMI and age at screening, were also found to be significantly associated with cancer status, underscoring the multifactorial nature of breast cancer risk. The model's predictive capability was further evidenced by an AUC of 0.63, indicating good performance. The study's implications are profound, offering a clinically significant tool for personalized breast cancer risk prediction and potentially enhancing screening strategies across diverse populations.

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