Highly integrated annotation and segmentation system for medical imaging

Abstract

A method for training a segmentation correction model includes performing an iterative model training process over a plurality of iterations. During each iteration, an initial segmentation estimate for an image is provided to a human annotators via an annotation interface. The initial segmentation estimate identifies one or more anatomical areas of interest within the image. Interactions with the annotation interface are automatically monitored to record annotation information comprising one or more of (i) segmentation corrections made to the initial segmentation estimate by the annotators via the annotation interface, and (ii) interactions with the annotation interface performed by the annotators while making the corrections. A base segmentation machine learning model is trained to automatically create a base segmentation based on the image. Additionally, a segmentation correction machine learning model is trained to automatically perform the segmentation corrections based on the image.