Abstract
Vertebral Body Growth Modulation (VBGM) allows to treat mild to severe spinal deformations by tethering vertebral bodies together, helping to preserve lower back flexibility. Forecasting the outcome of VBGM from skeletally immature patients remains elusive with several factors involved in corrective vertebral tethering, but could help orthopaedic surgeons plan and tailor VBGM procedures prior to surgery. We introduce a novel intra-operative framework forecasting the outcomes during VBGM surgery in scoliosis patients. The method is based on spatial-temporal corrective networks, which learns the similarity in segmental corrections between patients and integrates a long-term shifting mechanism designed to cope with timing differences in onset to surgery dates, between patients in the training set. The model captures dynamic geometric dependencies in scoliosis patients, as well as ensuring long-term dependancy with temporal dynamics in curve evolution. The loss function of the network introduces a regularization term based on learned group-average piecewise-geodesic path to ensure the generated corrective transformations are coherent with regards to the observed evolution of spine corrections at follow-up exams. The network was trained on 695 3D spine models and tested on 72 patients using a set of pre-operative spine reconstructions as inputs. The spatio-temporal network predicted outputs with errors of \(2.1 \pm 0.9\) mm in 3D anatomical landmarks, and yielding geometries similar to ground-truth reconstructions.
Supported by the Canada Research Chairs and NSERC Discovery Grants.
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Mandel, W., Parent, S., Kadoury, S. (2020). Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_73
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