Open-Source Automatic Whole and Subchondral Bone Segmentation using a Deep-Learning-Based Framework, DOSMA

Motivation: [18F]NaF PET imaging is a promising technique to study the role of bone metabolism in joint diseases such as osteoarthritis. To ease the corresponding burden of segmentations and data analysis, we developed an automated pipeline. Goal(s): We developed a new automated pipeline for knee bone segmentations. We validated the creation of subchondral bone masks for applications to [18F]NaF PET imaging by measuring changes in PET measures. Approach: We developed an automated segmentation pipeline for bone segmentations and validated the results for PET imaging. Results: DOSMA automated segmentations perform highly for bones and show applicability for quantitative musculoskeletal analysis. Impact: Our automated bone segmentation and PET data analysis pipeline enables a streamlined way of automating PET-MRI processing, including registration, segmentation, quantitative mapping, and visualization of outcome measures.