Spine Segmentation Using Articulated Shape Models

Authors:
Tobias Klinder;Robin Wolz;Cristian Lorenz;Astrid Franz;Jörn Ostermann
Affiliations:
Institut für Informationsverarbeitung, Leibniz University of Hannover, Germany and Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Germany;Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Germany;Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Germany;Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Germany;Institut für Informationsverarbeitung, Leibniz University of Hannover, Germany
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

Citing 6
Cited 2

Shape Constrained Deformable Models for 3D Medical Image Segmentation

IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Coupled Parametric Active Contours

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated model-based rib cage segmentation and labeling in CT images

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Quantitative vertebral morphometry using neighbor-conditional shape models

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Principal spine shape deformation modes using riemannian geometry and articulated models

AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Constrained surface evolutions for prostate and bladder segmentation in CT images

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications

Surface/Volume-Based Articulated 3D Spine Inference through Markov Random Fields

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Nonlinear embedding towards articulated spine shape inference using higher-order MRFs

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. Currently, most approaches are focused on the modeling and segmentation of individual objects. In case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. In this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. The global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. Adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. With the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic CT images improving former results.