Volume of hyperintense inflammation (VHI): a deep learning-enabled quantitative imaging biomarker of inflammation load in spondyloarthritis
Carolyna Hepburn, Alexis Jones, Alan Bainbridge, Coziana Ciurtin, Juan Eugenio Iglesias, HUI ZHANG, Margaret A. Hall-Craggs, Timothy JP Bray, . Joint senior authorship.
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/c-hepburn/Bone_MRIOfficialIn paperpytorch★ 3
Abstract
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.