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Postoperative 3D spine reconstruction by navigating partitioning
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The postoperative evaluation of scoliosis patients undergoing corrective treatment
is an important task to assess the strategy of the spinal surgery. Using accurate
geometric models of the patient’s spine is essential to measure
longitudinal changes in the patient’s anatomy. On the other hand, reconstructing the
spine in 3D from postoperative radiographs is a
challenging problem due to the presence of instrumentation (metallic rods and
screws) occluding vertebrae on the spine.
This paper describes the reconstruction problem by searching for the optimal
within a manifold space of articulated spines learned from a training
dataset of pathological cases who underwent surgery. The manifold structure is
implemented based on a multilevel manifold ensemble to structure the data,
incorporating connections between nodes within a single manifold, in addition
to connections between different multilevel manifolds,
representing subregions with similar characteristics.
The reconstruction pipeline was evaluated on x-ray datasets from
both preoperative patients and patients with spinal surgery. By comparing the
method to ground-truth models, a 3Dreconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30
postoperative scoliotic patients, while handling patients with highly deformed
This paper illustrates how this manifoldmodel can
accurately identify similar spine models by navigating in the low-dimensional space,
as well as computing nonlinear charts within local neighborhoods of the embedded
space during the testing phase. This technique allows postoperative follow-ups
of spinal surgery using personalized 3D spine models and assess
surgical strategies for spinal deformities.
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