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

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.