Development and evaluation of automated organ segmentation models for abdominal ultrasound in injured children

Background: Accurate organ segmentation in pediatric focused assessment with sonography in trauma (FAST) is an important prerequisite for automated image analysis and image quality assessment, but manual segmentation is time-intensive, subject to inter- and intra-observer variability, and limits the scalability of deep learning dataset development. Although deep learning has shown promise for ultrasound organ segmentation, its application to pediatric FAST has not been evaluated. Objective: To describe the development and evaluation of deep learning models to automatically localize and segment

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