Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
This addresses the need for reliable disease progression assessment in clinical spine imaging, though it is incremental as it builds on existing registration techniques.
The paper tackled the problem of automatically aligning longitudinal spine CT scans to monitor lesion growth, achieving accurate registration with an average Hausdorff distance of 0.65 mm and Dice score of 0.92 on a dataset of 111 registrations from 5 patients.
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.