Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
Malika Sanhinova Nazim Haouchine Steve D. Pieper William M. Wells III Tracy A. Balboni Alexander Spektor Mai Anh Huynh Jeffrey P. Guenette Bryan Czajkowski Sarah Caplan Patrick Doyle Heejoo Kang David B. Hackney Ron N. Alkalay
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.