Classification of organ of interest shapes for autosegmentation quality assurance

Abstract

Embodiments described herein provide for receiving a second image comprising an overlay depicting an organ-at-risk (OAR) segmentations. The overlay is generated by a first machine learning model based on a first image depicting the anatomical region of a current patient. A second machine learning model receives the second image and set of third images depicting prior patient OAR segmentations on which the second machine learning model was trained. The second machine learning model classifies the second image as one of a set of class names and characterizes the extent to which the second image is similar to, or dissimilar to, images with the same class name in the set of third images. The characterization may be based on outputs of internal layers of the second machine learning model. Dimensionality reduction may be performed on the outputs of the internal layers to present the outputs in a form comprehendible by humans.