Paper
11 March 2008 Use of a CT statistical deformation model for multi-modal pelvic bone segmentation
Stephen Thompson, Graeme Penney, Damien Buie, Prokar Dasgupta, Dave Hawkes
Author Affiliations +
Abstract
We present a segmentation algorithm using a statistical deformation model constructed from CT data of adult male pelves coupled to MRI appearance data. The algorithm allows the semi-automatic segmentation of bone for a limited population of MRI data sets. Our application is pelvic bone delineation from pre-operative MRI for image guided pelvic surgery. Specifically, we are developing image guidance for prostatectomies using the daVinci telemanipulator. Hence the use of male pelves only. The algorithm takes advantage of the high contrast of bone in CT data, allowing a robust shape model to be constructed relatively easily. This shape model can then be applied to a population of MRI data sets using a single data set that contains both CT and MRI data. The model is constructed automatically using fluid based non-rigid registration between a set of CT training images, followed by principal component analysis. MRI appearance data is imported using CT and MRI data from the same patient. Registration optimisation is performed using differential evolution. Based on our limited validation to date, the algorithm may outperform segmentation using non-rigid registration between MRI images without the use of shape data. The mean surface registration error achieved was 1.74 mm. The algorithm shows promise for use in segmentation of pelvic bone from MRI, though further refinement and validation is required. We envisage that the algorithm presented could be extended to allow the rapid creation of application specific models in various imaging modalities using a shape model based on CT data.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen Thompson, Graeme Penney, Damien Buie, Prokar Dasgupta, and Dave Hawkes "Use of a CT statistical deformation model for multi-modal pelvic bone segmentation", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141O (11 March 2008); https://doi.org/10.1117/12.770254
Lens.org Logo
CITATIONS
Cited by 16 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Magnetic resonance imaging

Data modeling

Bone

Image segmentation

Image registration

Statistical modeling

Computed tomography

Back to Top