Towards Deep Learning-assisted Quantification of Inflammation in Spondyloarthritis: Intensity-based Lesion Segmentation

Purpose: To develop a semi-automated, AI-assisted workflow for segmentation of inflammatory lesions on STIR MRI of sacroiliac joints (SIJs) in adult patients with axial spondyloarthritis. Methods: Baseline human performance in manual segmentation of inflammatory lesions was first established in eight patients with axial spondyloarthritis recruited within a prospective study conducted between April 2018 and July 2019. To improve readers' consistency a semi-automated procedure was developed, comprising (1) manual segmentation of 'normal bone' and 'disease' regions (2) automatic segmentation of lesions, i.e., voxels in the disease region with outlying intensity with respect to the normal bone, and (3) human intervention to remove erroneously segmented areas. Segmentation of disease region (subchondral bone) was automated via supervised deep learning; 200 image slices (eight subjects) were used for algorithm training with cross validation, 48 (two subjects) - for testing and 500 (20 subjects) - for evaluation based on visual assessment. The data, code, and model are available at https://github.com/c-hepburn/Bone_MRI. Human and model performance were assessed in terms of Dice coefficient. Results: Intra-reader median Dice coefficients, evaluated from comparison of manual segmentation trials of inflammatory lesions, were 0.63 and 0.69 for the two readers, respectively. Inter-reader median Dice was in the range of 0.53 to 0.56 and increased to 0.84 using the semi-automated approach. Deep learning model ensemble showed average Dice of 0.94 in subchondral bone segmentation. Conclusions: We describe a semi-automated, AI-assisted workflow which improves the objectivity and consistency of radiological segmentation of inflammatory load in SIJs.