Multimodal and Multiparametric Spatial Segmentation of Spine

Abstract

Scoliosis embodies the most frequent three dimensional spinal deformities in children. Only timely treatment during the growth of the spine may significantly reduce related health problems inflicted by the deformity on adults. The results obtained via conservative therapy are problematic, and a certain degree of curvature already requires surgical treatment that currently consists in repeated spinal surgeries posing a high risk of complications. The aim is to use a spine model for computer based simulation of changes in the stress on the spine during idiopathic and syndromic deformity correction via vertebral osteotomy. One of the goals of the work was resampling and registration of the CT and MR image sequences. CT volumes provided solid contrast. Due to the low quality of the MRI volumes image data CT were used as a reference for gaining properly segmented groups of vertebrae. The concern of this work is a development of an extension module for 3D Slicer platform. The core of the module is an implementation of a Support Vector Machines classifier, which is used for segmentation of the vertebra image data provided by the University Hospital Brno. The resampling and registration techniques has been optimized to process the MR and CT data of all imaging sequences. CT volumes provide solid contrast and were used as a reference for gaining properly segmented groups of vertebrae. The extension module scripted in Python language is freely accessible as a 3D Slicer tool and can be used in the future to process new datasets.