Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis

Journal article

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network.

Authors: Zheng, J-Q; Lim, NH; Papież, BW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Computerized Medical Imaging and Graphics
• Volume: 106
• Article number: 102204
• Publication date: 2023-02-24
• Acceptance date: 2023-02-14
• DOI: 10.1016/j.compmedimag.2023.102204
• EISSN: 1879-0771
• ISSN: 0895-6111
• Language: English
• Keywords: deep learning; mutual attention; image registration; FFR; tibiae CT