Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.

Cristina Alfaro^1,2, Gabriel Guerra^3,4, Claudia Prieto^4,5, Domingo Mery^4,6

¹Department of Medical Technology, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000000, Chile.

²Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

³PhD Program in Health Sciences and Engineering, Universidad de Valparaíso, Valparaíso 2340000, Chile.

⁴Millennium Institute for Intelligent Healthcare Engineering iHealth, Santiago 7820436, Chile.

⁵Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

⁶Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

PubMed PMC DOI PDF

Abstract

Background/Objectives: Digital breast tomosynthesis (DBT) provides quasi-three-dimensional breast imaging, but volumetric segmentation model development is constrained by limited expert-annotated clinical data. This proof-of-concept study evaluated the feasibility of synthetic-to-clinical breast tumor segmentation using synthetic model development, limited clinical fine-tuning, and heterogeneous e…

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