2021/06/21 by C. Hepburn, Hepburn, Carolyna, Alexis Jones +15
Medicine · #Bone and Joint Diseases #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Rheumatoid Arthritis Research and Therapies #Spondyloarthritis Studies and Treatments #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.11343
openalex publication_date 2021/06/21 · openalex created_date 2022/08/17 · openalex updated_date 2026/07/28
Short inversion time inversion recovery (STIR) MRI is widely used in clinical practice to identify and quantify inflammation in axial spondyloarthritis. However, assessment of STIR images is limited by the need for qualitative evaluation, which depends on observer experience and expertise, creating substantial variability in inflammation assessments. To address this problem, we developed a deep learning-enabled, semiautomated workflow for segmentation of inflammatory lesions, whereby an initial segmentation is generated automatically and a radiologist then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which we propose as a quantitative imaging biomarker of inflammation load in spondyloarthritis. The data, code and models used in the study are available at https://github.com/c-hepburn/BoneMRI.