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Abstract #1713

Cartilage Auto-Segmentation of 3D T2* GRE Sequence in 7T High-Resolution 3D MRI

Eisa Hedayati1, Abdul Wahed Kajabi1, Karsten Knutsen1, Collin Steinberger1, Abhinav Lamba1, Luke Tollefson2, Gregor Metzger1, Robert LaPrade1, and Jutta Ellermann1
1Radiology, University of Minnesota, Minneapolis, MN, United States, 2Twin Cities Orthopedics, Minneapolis, MN, United States

Synopsis

Keywords: Analysis/Processing, Segmentation, 7T, Cartilage, Augmentation, T2*, 3D MRI

Motivation: Articular cartilage degeneration is the hallmark of knee osteoarthritis.

Goal(s): Enable fast and robust assessment of degenerative changes in knee articular cartilage through auto-segmentation of high-resolution 7T 3D T2*-weighted MRI sequences.

Approach: Train a supervised residual U-Net model on a limited ground truth dataset to generate preliminary segmentations, easing manual segmentation efforts and expanding the data pool efficiently.

Results: The auto-segmentation achieved an overall mean Dice score of.826, 81.5 for the control cohort, 0.83 for patients with medial meniscus posterior root tears, and 0.817 for post-repair MRI of the same patients.

Impact: This method accelerates cartilage segmentation in 3D T2*-weighted MRI, reducing manual correction, speeding ground truth creation, potentially supporting quantitative analysis, and enhancing efficiency in cartilage assessment for knee osteoarthritis.

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Keywords