Bayesian Landmark-based Shape Analysis of Tumor Pathology Images.

ABSTRACT: Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this paper, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11395925 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature