Elanchezhian Somasundaram, Zachary Taylor, Vinicius V Alves, Lisa Qiu, Benjamin Fortson, Neeraja Mahalingam, Jonathan Dudley, Hailong Li, Samuel L Brady, Andrew T Trout, Jonathan R Dillman
Background: Deep-learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. Objective: To develop and validate deep-learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. Methods: This retrospective study developed and validated deep-learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤18) datasets (n=483) and three public datasets comprising pediatric and adult examinations with various pathologies (n=1248)...
May 1, 2024: AJR. American Journal of Roentgenology