Articular cartilage damage is a primary outcome of pre-clinical and clinical studies evaluating meniscal and cartilage repair or replacement techniques. Recent studies have quantitatively characterized India Ink stained cartilage damage through light reflectance and the application of local or global thresholds. We develop a method for the quantitative characterisation of inked cartilage damage with improved generalisation capability, and compare its performance to the threshold-based baseline approach against gold standard labels. The Trainable WEKA Segmentation (TWS) tool (Arganda-Carreras et al., 2017) available in Fiji (Rueden et al., 2017) was used to train two separate Random Forest classifiers to automatically segment cartilage damage on ink stained cadaveric ovine stifle joints. Gold standard labels were manually annotated for the training, validation and test datasets for each of the femoral and tibial classifiers. Each dataset included a sample of medial and lateral femoral condyles and tibial plateaus from various stifle joints, selected to ensure no overlap across datasets according to ovine identifier. Training was performed on the training data with the TWS tool using edge, texture and noise reduction filters selected for their suitability and performance. The two trained classifiers were then applied to the validation data to output damage probability maps, on which a threshold value was calibrated. Model predictions on the unseen test set were evaluated against the gold standard labels using the Dice Similarity Coefficient (DSC) – an overlap-based metric, and compared with results for the baseline global threshold approach applied in Fiji as shown in Figures 1 and 2.Objectives
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