Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
<|reference_start|>Registration of Longitudinal Spine CTs for Monitoring Lesion Growth: 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.<|reference_end|>
@article{sanhinova2024registration,
    title={Registration of Longitudinal Spine CTs for Monitoring Lesion Growth},
    author={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},
    journal={arXiv preprint arXiv:2402.09341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09341},
    primaryClass={eess.IV cs.CV}
}