3D segmentation of rodent brains using deformable models and variational methods

Shaoting Zhang, Jinghao Zhou, Xiaoxu Wang, Sukmoon Chang, Dimitris N. Metaxas, George Pappas, Foteini Delis, Nora D. Volkow, Gene Jack Wang, Panayotis K. Thanos, Chandra Kambhamettu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains.tric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneratioin. Although various segmentation methods in clinical studies have been proposed, many of them require a priori knowledge about the locations of the structures of interest, which prevents the fully automatic segmentation. Besides, the topological changes of structures are difficult to detect. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic 3D functional segmentation of rodent brain MR images. The presented method is based on active shape model (ASM), Metamorph models and variational techniques. It focuses on detecting the topological changes of brain structures based on a novel ratio criteria. The mean successful rate of the topological change detection shows 86.6% accuracy compared to the expert identified ground truth.

Original languageEnglish (US)
Title of host publication2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Pages94-100
Number of pages7
DOIs
StatePublished - Nov 20 2009
Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Other

Other2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
CountryUnited States
CityMiami, FL
Period6/20/096/25/09

Fingerprint

Brain
Variational techniques
Rodentia

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Zhang, S., Zhou, J., Wang, X., Chang, S., Metaxas, D. N., Pappas, G., ... Kambhamettu, C. (2009). 3D segmentation of rodent brains using deformable models and variational methods. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (pp. 94-100). [5204051] (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009). https://doi.org/10.1109/CVPR.2009.5204051
Zhang, Shaoting ; Zhou, Jinghao ; Wang, Xiaoxu ; Chang, Sukmoon ; Metaxas, Dimitris N. ; Pappas, George ; Delis, Foteini ; Volkow, Nora D. ; Wang, Gene Jack ; Thanos, Panayotis K. ; Kambhamettu, Chandra. / 3D segmentation of rodent brains using deformable models and variational methods. 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. 2009. pp. 94-100 (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009).
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abstract = "3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains.tric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneratioin. Although various segmentation methods in clinical studies have been proposed, many of them require a priori knowledge about the locations of the structures of interest, which prevents the fully automatic segmentation. Besides, the topological changes of structures are difficult to detect. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic 3D functional segmentation of rodent brain MR images. The presented method is based on active shape model (ASM), Metamorph models and variational techniques. It focuses on detecting the topological changes of brain structures based on a novel ratio criteria. The mean successful rate of the topological change detection shows 86.6{\%} accuracy compared to the expert identified ground truth.",
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Zhang, S, Zhou, J, Wang, X, Chang, S, Metaxas, DN, Pappas, G, Delis, F, Volkow, ND, Wang, GJ, Thanos, PK & Kambhamettu, C 2009, 3D segmentation of rodent brains using deformable models and variational methods. in 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009., 5204051, 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 94-100, 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, United States, 6/20/09. https://doi.org/10.1109/CVPR.2009.5204051

3D segmentation of rodent brains using deformable models and variational methods. / Zhang, Shaoting; Zhou, Jinghao; Wang, Xiaoxu; Chang, Sukmoon; Metaxas, Dimitris N.; Pappas, George; Delis, Foteini; Volkow, Nora D.; Wang, Gene Jack; Thanos, Panayotis K.; Kambhamettu, Chandra.

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. 2009. p. 94-100 5204051 (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Zhang S, Zhou J, Wang X, Chang S, Metaxas DN, Pappas G et al. 3D segmentation of rodent brains using deformable models and variational methods. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. 2009. p. 94-100. 5204051. (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009). https://doi.org/10.1109/CVPR.2009.5204051