  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/Bone_MRI.
