Volume of hyperintense inflammation (VHI): a deep learning-enabled quantitative imaging biomarker of inflammation load in spondyloarthritis

TL;DR

This paper introduces a deep learning-based semi-automated method to quantify inflammation in spondyloarthritis using MRI, providing a reliable biomarker called VHI that reduces observer variability.

Contribution

The study presents a novel deep learning workflow for segmentation of inflammatory lesions in MRI, enabling quantitative assessment of inflammation load in spondyloarthritis.

Findings

Automated segmentation of inflammation achieved high accuracy.

The VHI biomarker correlates with disease severity.

Workflow reduces variability in inflammation assessment.

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.