Abstract
In this paper, a former method has been adapted to perform vertebra segmentations for the 2nd Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014). A statistical Shape Models (SSM) of each lumbar vertebra was created for the segmentation step. From manually placed intervertebral discs centres, the similarity parameters are computed to initialise the vertebra shapes. The segmentation is performed by iteratively deforming a mesh inside the image intensity and then projecting it into the SSM space until convergence. Afterwards, a relaxation step based on B-spline is applied to overcome the SSM rigidity. The deformation of the mesh, within the image intensity, is performed by displacing each landmark along the normal direction of the surface mesh at the landmark position seeking a minimum of a cost function based on a set of trained features. The organisers tested the performance of our method with a dataset of five patients, achieving a global mean Dice Similarity Index (DSI) of 93.4 %. Results were consistent and accurate along the lumbar spine 93.8, 93.9, 93.7, 93.4 and 92.1 %, from L1 to L5.
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References
Roberts, M.G., Cootes, T.F., Pacheco, E., Oh, T., Adams, J.E.: Segmentation of lumbar vertebrae using part-based graphs and active appearance models. In: Lecture notes in computer science (MICCAI), pp. 1017–1024. Springer (2009)
Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13(3), 471 (2009)
Weese, J., Kaus, M., Lorenz, C., Lobregt, S., Truyen, R., Pekar, V.: Shape constrained deformable models for 3D medical image segmentation. Inf. Process. Med. Imaging pp. 380–387 (2001)
Castro-Mateos, I., Pozo, J.M., del Rio, L., Eltes, P., Lazary, A., Frangi, A.F.: 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images. Manuscript accepted for publication in Physics in Medicine and Biology (2014)
Cootes, T.F., Taylor, C.J., Graham, J.: Active shape models- their training and applications. Comput. Vis. Image Under 61(1), 38–59 (1995)
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Castro-Mateos, I., Pozo, J.M., Lazary, A., Frangi, A. (2015). 3D Vertebra Segmentation by Feature Selection Active Shape Model. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_22
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DOI: https://doi.org/10.1007/978-3-319-14148-0_22
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