Abstract #0809

Generalizability of Deep-Learning Segmentation Algorithms for Measuring Cartilage and Meniscus Morphology and T2 Relaxation Times

Andrew M Schmidt¹, Arjun D Desai¹, Lauren E Watkins², Hollis Crowder³, Elka B Rubin¹, Valentina Mazzoli¹, Quin Lu⁴, Marianne Black^1,3, Feliks Kogan¹, Garry E Gold^1,2, Brian A Hargreaves^1,5, and Akshay S Chaudhari^1,6

¹Radiology, Stanford University, Stanford, CA, United States, ²Bioengineering, Stanford University, Stanford, CA, United States, ³Mechanical Engineering, Stanford University, Stanford, CA, United States, ⁴Philips Healthcare North America, Gainesville, FL, United States, ⁵Electrical Engineering, Stanford University, Stanford, CA, United States, ⁶Biomedical Data Science, Stanford University, Stanford, CA, United States

Automated segmentation using deep-learning can expedite segmentation tasks, but algorithm generalizability to unseen datasets is unknown. Here, we used two knee segmentation algorithms, each trained separately on Osteoarthritis Initiative double-echo steady-state (DESS) scans and quantitative DESS (qDESS) scans, to segment cartilage and meniscus from qDESS datasets from four independent studies. We compared manual-vs-automatic segmentation accuracy for morphology and T₂ map variations. We show that OAI-DESS-trained models may be suitable for quantifying relaxometry parameters in qDESS datasets but likely require fine-tuning to accurately quantify cartilage morphology. In contrast, qDESS-trained models generalize well to additional qDESS datasets for both morphology and T₂.

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