Automated Intervertebral Disc Segmentation Using Probabilistic Shape Estimation and Active Shape Models
Automated segmentation of intervertebral discs (IVDs) from magnetic resonance imaging has the potential to enhance the efficiencies of radiological investigations of large clinical and research imaging datasets. This work presents an automated method for
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School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia [email protected], [email protected], [email protected] 2 The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia [email protected] 3 School of Human Movement Studies, University of Queensland, Brisbane, Australia [email protected]
Abstract. Automated segmentation of intervertebral discs (IVDs) from magnetic resonance imaging has the potential to enhance the efficiencies of radiological investigations of large clinical and research imaging datasets. This work presents an automated method for localization and 3D segmentation of IVDs that is applied to magnetic resonance imaging of the thoraco-lumbar spine as part of the segmentation challenge at the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. Our initialization method involves multi-atlas registration and a hierarchical conditional shape regression for localization of all imaged lumbar and thoracic discs, and active shape model based 3D segmentation. Comparisons between manual (ground truth) and automated segmentation of 105 disc volumes (T11/T12 - L5/S1) revealed a mean Dice score of 0.896 ± 0.024 and mean absolute square distance of 0.642 ± 0.169 mm. Our automated segmentation approach provided accurate segmentation of IVDs from turbo spine echo images which are highly competitive with leading state-of-the-art 3D segmentation techniques.
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Introduction
Spine-related disorders account for the largest proportion of musculoskeletal complaints in industrialized countries [1,2]. Magnetic resonance imaging (MRI) allows highly detailed, multiplanar investigations of spine pathologies, such as intervertebral disc (IVD) prolapse, herniation and degeneration [3]. Informatic tools offer significant opportunities for improving the efficiency of radiological c Springer International Publishing Switzerland 2016 T. Vrtovec et al. (Eds.): CSI 2015, LNCS 9402, pp. 150–158, 2016. DOI: 10.1007/978-3-319-41827-8 15
Automated Intervertebral Disc Segmentation
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assessment of the spine by reducing the time- and expertise-intensive encumbrances of tedious tasks as required in three-dimensional (3D) segmentation and measurement of anatomical structures. Precise segmentation of IVDs is a prerequisite for many clinical applications (diagnosis, treatment planning and evaluation), and automated segmentation has the potential to enhance the efficiencies of radiological investigations of large clinical and research imaging datasets. This work presents a fully automated algorithm for 3D segmentation of lumbar and thoracic IVDs from sagittal T2-weighted MRI scans and evaluates it on a publicly available dataset as part of the challenge on “Automatic IVD localization and segmentation from 3D T2 MRI data” at the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. The current method exte
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