Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis
Abstract
In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around “suspicious findings”. Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique “self-steering tomosynthesis.” A U-Net was used to classify the low-dose projections into “risk classes” in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 10, 2023
- Source ID
- 10.3390/tomography9030092
Entities
People
- Andrew D. A. Maidment
- Bruno Barufaldi
- Giulia Carvalhal
- Joao P. V. Teixeira
- Raymond J Acciavatti
- Telmo M. Silva Filho
- Thaís Gaudencio do Rêgo
- Yann N. G. Da Nobrega
- Yuri Malheiros
Organizations
- American Association of Physicists in Medicine
- Breast Cancer Alliance
- Burroughs Wellcome Fund
- Federal University of Paraíba
- National Institutes of Health
- United States Department of Defense
- University of Bristol
- University of Pennsylvania