Accurate and reliable registration of longitudinal spine images is essential
for assessment of disease progression and surgical outcome. Implementing a
fully automatic and robust registration is crucial for clinical use, however,
it is challenging due to substantial change in shape and appearance due to
lesions. In this paper we present a novel method to automatically align
longitudinal spine CTs and accurately assess lesion progression. Our method
follows a two-step pipeline where vertebrae are first automatically localized,
labeled and 3D surfaces are generated using a deep learning model, then
longitudinally aligned using a Gaussian mixture model surface registration. We
tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3,
6, and 12 months follow-ups leading to 111 registrations. Our experiment showed
accurate registration with an average Hausdorff distance of 0.65 mm and average
Dice score of 0.92.

实现自动且鲁棒的纵向脊柱图像配准对于评估疾病进展和手术结果至关重要，本文提出了一种新颖的方法，通过深度学习模型自动定位、标记和生成三维表面，然后使用高斯混合模型表面配准实现纵向脊柱 CT 图像对齐并准确评估病变进展，实验证明该方法在 37 个椎体、5 名患者的基线和 3、6、12 个月后的随访图像上显示出了平均 Hausdorff 距离为 0.65 毫米和 Dice 分数为 0.92 的准确配准。