System and methods for quantifying uncertainty of segmentation masks produced by machine learning models

Abstract

Systems and methods are provided for quantifying uncertainty of segmentation mask predictions made by machine learning models, where the uncertainty may be used to streamline an anatomical measurement workflow by automatically identifying less certain caliper placements. In one example, the current disclosure teaches receiving an image including a region of interest, determining a segmentation mask for the region of interest using a trained machine learning model, placing a caliper at a position within the image based on the segmentation mask, determining an uncertainty of the position of the caliper, and responding to the uncertainty of the position of the caliper being greater than a pre-determined threshold by displaying a visual indication of the position of the caliper via a display device and prompting a user to confirm or edit the position of the caliper.