Meeting Banner
Abstract #2468

Generalizability of nnU-Net for automatic segmentation of knee MRI

Heather Hanegraaf1, Rianne A. van der Heijden1,2, Edwin H.G. Oei1, Marienke van Middelkoop3, Stefan Klein1, and Jukka Hirvasniemi1
1Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands, 2Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Department of General Practice, Erasmus MC University Medical Center, Rotterdam, Netherlands

Synopsis

Keywords: Osteoarthritis, SegmentationTo investigate the generalizability of deep learning segmentation models, three different nnU-Nets (2D, 3D, and ensemble) were trained on the OAI dataset and tested on a different dataset. In addition to the nnU-Nets trained on the original OAI data, the style of the test set was transferred to the training set using a CycleGAN method and the nnU-Nets were trained again. Depending on the tissue, the 3D nnU-Net or the ensemble trained on the original or stylized training data had the highest segmentation accuracy in the test set. The results indicate that nnU-Net may generalize well to independent datasets.

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.

Click here for more information on becoming a member.

Keywords