Abstract #1720

Validation of an Automated Open Source Pipeline for Comprehensive Knee MRI Segmentation and Measurement of Quantitative Outcomes

Francesca Belibi¹, Vyoma Sahani¹, Yael Vainberg¹, Ananya Goyal¹, Ashley Williams², Constance Chu², Rune Pedersen³, Bryan Haddock³, Akshay Chaudhari¹, Feliks Kogan¹, and Anthony Gatti¹

¹Radiology, Stanford University, Stanford, CA, United States, ²Orthopedic Surgery, Stanford University, Stanford, CA, United States, ³Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University, Rigshospitalet, Denmark

Synopsis

Keywords: Analysis/Processing, Segmentation, Cartilage, Quantitative measures

Motivation: Our motivation was to determine a way to decrease the time dedicated to tissue segmentation and data analysis.

Goal(s): Our goal was to test the validity of the DOSMA automated segmentation pipeline for OA analysis.

Approach: Building on previous DOSMA frameworks we compared the segmentation metrics and cartilage qualitative metrics associated with OA risk of two manual readers with the automatic reader.

Results: The study demonstrated performance of the DOSMA automated pipeline for bone and cartilage segmentation and quantitative analysis of musculoskeletal tissues that was on par or better than inter-reader performance.

Impact: Automated segmentation and analysis of bone and cartilage have the potential to greatly improve the translation potential of quantitative MSK MRI biomarkers.

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