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

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 invo...

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Main Authors: Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Embryonic cartilage segmentation
Multi-age image data
Micro-CT
Conditional training
Self-attention
Online Access:https://doi.org/10.1038/s41598-025-87797-9
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https://doi.org/10.1038/s41598-025-87797-9

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