Keywords: Analysis/Processing, AI/ML Software
Motivation: Machine learning segmentation is becoming more widely used and applied in longitudinal studies to assess changes in organ volume, with organ masks applied to evaluate quantitative metrics.
Goal(s): To evaluate the abdominal organ segmentation consistency of nnU-Net.
Approach: nnU-Net segmentation models were trained to segment abdominal organs. These models were applied to assess intra-session and inter-session repeatability of organ volume measures.
Results: Intra-scan session variance for all organs was extremely low (<1.4 %). Inter-scan variance was low across most organs (<6 %), with the exceptions of some adipose tissue segmentations and kidney cortex/medulla segmentations.
Impact: Assessment of intra- and inter- scan session reproducibility provides an understanding of the detectable change in organ volume in longitudinal studies and the accuracy of masks used to derive associated quantitative metrics.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords