Validation of a Novel Semiautomated Segmentation Method for MRI Detection of Cartilage-Related Bone Marrow Lesions

To determine the relationship of bone marrow lesions (BMLs) with phenomena such as clinical symptoms, histological subchondral bone damage, and development of osteoarthritis, a reliable and reproducible method to localize and quantify BMLs accurately is indispensable. Therefore, the goal of the current study was to develop and validate a novel semiautomated segmentation method based on the KNN classification technique on T2-weighted (T2w) SPIR and proton density-weighted (PDw) magnetic resonance images (MRIs), as this would provide an accurate, reliable, and reproducible tool.

Twenty PDw and T2w SPIR MRIs were selected and manually segmented as a learning set for the software system. The manual segmentations were considered the gold standard. Automated segmentation based on the KNN classification technique was carried out on the same MRIs. To determine the accuracy and validity of the system, the automated segmentations were compared to the gold standard using the Dice Similarity Index (DSI).

The KNN classification system resulted both visually and statistically in an accurate segmentation of BMLs on T2w SPIR MRIs with an excellent mean optimal DSI of 0.702 (±0.202; range, 0.409-0.908). Elimination of specific areas smaller than 10 voxels improved the accuracy. The accuracy was independent of BML size. The segmentation of BMLs on PDw MRIs was less reliable with a mean optimal DSI of 0.536 (±0.156).

Although the applicability of this method is limited on PDw MRIs, the KNN classification system provides an accurate, reliable, and reproducible tool for semiautomated segmentation of BMLs in T2w SPIR MRIs of the knee.