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

Abstract

Objective: 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.

Materials and methods: 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).

Results: 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).

Conclusion: 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.

Keywords: KNN classification; MRI; bone marrow edema; bone marrow lesions; cartilage; quantitative method; segmentation.

Figure 1.

Comparison of a binary segmentation (Seg) with the manual segmentation (gold standard; Ref), with the correctly classified voxels (Overlap), the false positives (Extra), and the false negatives (Miss).

Figure 2.

An example to illustrate the meaning of the probabilistic measures and to provide a better intuitive understanding of the KNN classification is presented. (A) Example of the original T2w SPIR magnetic resonance images (MRI) containing a bone marrow lesion (BML). Using the KNN classification, a probability map of the BML is generated (B). Accordingly, a binary segmentation is generated (red) after applying the mean optimal threshold (C). This segmentation can be compared with the gold standard (green; D). (E) Shows the result after discarding blobs of less than 10 voxels (cyan).

Figure 3.

Dice Similarity Indices of binary bone marrow lesion (BML) segmentations of all patients for T2-weighted SPIR and proton density (PD)–weighted magnetic resonance images (MRIs) as function of the threshold.

Figure 4.

Dice Similarity Indices of binary bone marrow lesion (BML) segmentations of all patients for T2-weighted SPIR and proton density (PD)–weighted magnetic resonance images (MRIs) as function of the signal-to-noise ratio.

Figure 5.

Receiver-operating characteristic (ROC) curves of classifications of all patients for T2w SPIR and proton density weighted (PDw) magnetic resonance images (MRIs) showing the relationship between the sensitivity and 1 – specificity for different thresholds.

Figure 6.

Dice Similarity Indices of binary bone marrow lesion (BML) segmentations of all patients for T2w SPIR as function of the BML volume (cc).