Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with Sparsely annotated data.

ABSTRACT: Osteochondrodysplasia, affecting 2-3% of newborns globally, is a group of bone and cartilage disorders that often result in head malformations, contributing to childhood morbidity and reduced quality of life. Current research on this disease using mouse models faces challenges since it involves accurately segmenting (precisely delineating) the developing cartilage in 3D micro-CT images of embryonic mice. Tackling this segmentation task with deep learning (DL) methods is laborious due to the big burden of manual image annotation, expensive due to the high acquisition costs of 3D micro-CT images, and difficult due to embryonic cartilage's complex and rapidly changing shapes. While DL approaches have been proposed to automate cartilage segmentation, most such models have limited accuracy and

SUBMITTER: Sapkota N 

PROVIDER: S-EPMC11785758 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature