Abstract
Active shape models (ASMs) have been established as robust model-based segmentation approaches and have been particularly relevant for objects ill-defined in image data. For example, the automatic segmentation of the optic pathway is almost impossible without shape models due to low contrast in MRI and local anatomical variability. However, traditional ASM is not optimal for complex or variable shapes segmentation due to its strong constraints. Herein, we introduce a weighted partitioned active shape model to improve the shape flexibility and robustness of ASMs and apply it to optic pathway (including the nerve, chiasm, and tract) segmentation. The strong constraints of ASM are relaxed by partitioning the whole shape into several subparts. In this way, the local shape variability can be captured and the number of training data can be reduced. Our novel weighted matching approach assigns a weight to each landmark point according to its appearance confidence, thus deforming the shape to reliable positions. In the application of optic pathway segmentation, the mean of root mean squared symmetric surface distance is 0.59 mm, which is about one voxel size.
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References
Heimann, T., et al.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13, 543–563 (2009)
Cootes, T.F., et al.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Lamecker, H., et al.: Segmentation of the liver using a 3D statistical shape model. Citeseer, Kyoto (2004)
de Bruijne, M., et al.: Adapting active shape models for 3D segmentation of tubular structures in medical images. Inf. Process. Med. Imaging 18, 136–147 (2003)
Zhao, Z., et al.: A novel 3D partitioned active shape model for segmentation of brain MR images. Med. Image Comput. Comput.-Assist. Interv. 3749, 221–228 (2005)
Davatzikos, C., et al.: Hierarchical active shape models, using the wavelet transform. IEEE Trans. Med. Imaging 22, 414–423 (2003)
Avery, R.A., et al.: Optic pathway gliomas. J. Neuroophthalmol. Off. J. North Am. Neuroophthalmol. Soc. 31, 269–278 (2011)
Schmitt, P., et al.: Effects of slice thickness and head rotation when measuring glioma sizes on MRI: in support of volume segmentation versus two largest diameters methods. J. Neurooncol. 112, 165–172 (2013)
Bekes, G., et al.: Geometrical model-based segmentation of the organs of sight on CT images. Med. Phys. 35, 735–743 (2008)
Noble, J.H., et al.: An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images. Med. Image Anal. 15, 877–884 (2011)
Reyes, M., et al.: Anatomical variability of organs via principal factor analysis from the construction of an abdominal probabilistic atlas. In: Proceedings IEEE International Symposium on Biomedical Imaging, vol. 2009, pp. 682–685 (2009)
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963)
Cootes, T.F., et al.: Statistical models of appearance for medical image analysis and computer vision. In: SPIE Medical Imaging, pp. 236–248 (2001)
Law, M.W., et al.: Efficient implementation for spherical flux computation and its application to vascular segmentation. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 18, 596–612 (2009)
Zhao, M., et al.: Shape evaluation for weighted active shape models. In: Proceedings of the Asian Conference on Computer Vision, pp. 1074–1079 (2004)
Ourselin, S., et al.: Robust registration of multi-modal images: towards real-time clinical applications. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part II. LNCS, vol. 2489. Springer, Heidelberg (2002)
Fang, Q., et al.: Tetrahedral mesh generation from volumetric binary and grayscale images. In: ISBI’09. IEEE International Symposium on, pp. 1142–1145 (2009)
Myronenko, A., et al.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–2275 (2010)
Rueckert, D., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999)
Acknowledgements
This project was supported in part by the Neurofibromatosis Institute at Children’s National and a philanthropic gift from the Government of Abu Dhabi to Children’s National Healthcare System.
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Yang, X. et al. (2014). Weighted Partitioned Active Shape Model for Optic Pathway Segmentation in MRI. In: Linguraru, M., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2014. Lecture Notes in Computer Science(), vol 8680. Springer, Cham. https://doi.org/10.1007/978-3-319-13909-8_14
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DOI: https://doi.org/10.1007/978-3-319-13909-8_14
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