Poster + Paper
2 April 2024 Registration of longitudinal spine CTs for monitoring lesion growth
Author Affiliations +
Conference Poster
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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, and Ron N. Alkalay "Registration of longitudinal spine CTs for monitoring lesion growth", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262O (2 April 2024); https://doi.org/10.1117/12.3006621
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KEYWORDS
Image registration

Spine

Data modeling

3D modeling

Automatic alignment

Bone

Computed tomography

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