Abstract: An automated post-processing tool to assess the quality of deep learning-based organ segmentation in medical images is described. According to an example, a method comprises determining, by a system comprising a processor, current values of defined features of respective segmentation masks generated for different anatomical structures included in medical image data via auto-segmentation of the medical image data. The method further comprises determining, by the system, respective measures of correspondence between the current values and corresponding reference values determined for the defined features, determining one or more measures of quality of the auto-segmentation based on the respective measures of correspondence, generating quality assessment report data for the auto-segmentation comprising the one or more measures of quality in standard format that can be displayed by standard clinical software.

Abstract: Methods and systems are provided for segmenting structures in medical images. In one embodiment, a method includes receiving an input dataset including a set of medical images, a structure list specifying a set of structures to be segmented, and a segmentation protocol, performing an input check on the input dataset, determining whether each medical image of the set of medical images has passed the input check and removing any medical images from the set of medical images that do not pass the input check to form a final set of medical images, segmenting each structure from the structure list using one or more segmentation models and the final set of medical images, receiving a set of segmentations output from the one or more segmentation models, processing the set of segmentations to generate a final set of segmentations, and displaying and/or saving in memory the final set of segmentations.

Abstract: An automated post-processing tool to assess the quality of deep learning-based organ segmentation in medical images is described. According to an example, a method comprises determining, by a system comprising a processor, current values of defined features of respective segmentation masks generated for different anatomical structures included in medical image data via auto-segmentation of the medical image data. The method further comprises determining, by the system, respective measures of correspondence between the current values and corresponding reference values determined for the defined features, determining one or more measures of quality of the auto-segmentation based on the respective measures of correspondence, generating quality assessment report data for the auto-segmentation comprising the one or more measures of quality in standard format that can be displayed by standard clinical software.