Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images

Abstract

Applying HR-pQCT to image the knee necessitates the development and validation of novel image analysis workflows. Here, we present and validate the first automated workflow for in vivo quantitative assessment of peri-articular bone density and microarchitecture in the knee. Segmentation models were first trained with radius and tibia images (N=2,598) then fine-tuned with knee images (N=131). Atlas-based registration was used to create medial and lateral contact surface masks, which were combined with bone segmentations to generate peri-articular regions of interest masks. The accuracy and precision of the workflow was assessed with an external validation dataset (N=128) and a triple-repeat measures dataset (N=29), respectively. Predicted and reference morphological parameters had linear coefficients of determination between 0.86 and 0.99, with moderate bias present in predictions of subchondral bone plate density and thickness. The average short-term precision RMS%CV estimates across all compartments and all morphological parameters ranged from 1.0 % to 2.9 %.

Background and Objective:

There is growing interest in applying HR-pQCT to image the knee, particularly in the study of osteoarthritis. This necessitates the development and validation of novel image analysis workflows tailored to knee HR-pQCT images. In this work, we present and validate the first fully automated workflow for in vivo quantitative assessment of peri-articular bone density and microarchitecture in the human knee.

Methods:

Bone segmentation models were trained by transfer learning with a large dataset of radius and tibia images (N=2,598) and fine-tuned on a knee image dataset (N=131). Tibia and femur atlases were created and atlas-based registration was used to identify medial and lateral contact surfaces. Morphological operations combined bone segmentations and atlas-generated contact surface masks to generate peri-articular regions of interest masks, in which standard morphological analysis was applied. The accuracy and precision of estimated morphological parameters was assessed with an external validation dataset containing femurs and tibiae (N=128) and a triple-repeat measures dataset containing only tibiae (N=29), respectively.

Results:

On the external validation dataset, predicted and reference morphological parameters showed excellent correspondence (0.86 $\le$ R${}^2$ $\le$ 0.99), with moderate bias present in predictions of subchondral bone plate density (−80 mg HA/cm${}^3$) and thickness (＋0.15 mm). With intra-participant rigid registration, the average short-term precision RMS%CV estimates across all compartments were 2.2 % and 2.8 % for subchondral bone plate density and thickness, respectively, and 1.1 %, 2.9 %, 1.0 %, and 2.9 % for trabecular density, separation, thickness, and number, respectively.

Conclusion:

We have developed and evaluated an automated workflow for peri-articular analysis of knee HR-pQCT images, integrating deep learning, atlas-based segmentation, and standard image processing approaches. The code, atlases, and models have been made freely available for other researchers to use, improve, or extend. Future work will focus on the application of the workflow to clinical data to investigate osteoarthritis etiology.