Segmentation of rodent brains from MRI based on a novel statistical structure prediction method

Jinghao Zhou; Sukmoon Chang; Shaoting Zhang; George Pappas; Michael Michaelides; Foteini Delis; Nora Volkow; Panayotis Thanos; Metaxas Dimitris Metaxas

doi:10.1109/ISBI.2009.5193093

Segmentation of rodent brains from MRI based on a novel statistical structure prediction method

Jinghao Zhou, Sukmoon Chang, Shaoting Zhang, George Pappas, Michael Michaelides, Foteini Delis, Nora Volkow, Panayotis Thanos, Metaxas Dimitris Metaxas

School of Science, Engineering & Technology (Harrisburg)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric 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 neurodegeneration. Although various segmentation methods in clinical studies have been proposed, most of them require a priori knowledge about the locations of the structures of interest, preventing the fully automatic segmentation. 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 functional segmentation of 2D rodent brain MR images. The presented method focuses on detecting the topological changes of brain structures based on a novel area ratio criteria. The mean successful rate of the detection method shows 89.4% accuracy compared to the expert-identified ground truth.

Original language	English (US)
Title of host publication	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2009
Pages	498-501
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2009.5193093
State	Published - 2009
Event	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States Duration: Jun 28 2009 → Jul 1 2009

Publication series

Name	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/Territory	United States
City	Boston, MA
Period	6/28/09 → 7/1/09

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2009.5193093

Cite this

Zhou, J., Chang, S., Zhang, S., Pappas, G., Michaelides, M., Delis, F., Volkow, N., Thanos, P., & Dimitris Metaxas, M. (2009). Segmentation of rodent brains from MRI based on a novel statistical structure prediction method. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 498-501). Article 5193093 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193093

Zhou, Jinghao ; Chang, Sukmoon ; Zhang, Shaoting et al. / Segmentation of rodent brains from MRI based on a novel statistical structure prediction method. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. pp. 498-501 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

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title = "Segmentation of rodent brains from MRI based on a novel statistical structure prediction method",

abstract = "Functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric 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 neurodegeneration. Although various segmentation methods in clinical studies have been proposed, most of them require a priori knowledge about the locations of the structures of interest, preventing the fully automatic segmentation. 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 functional segmentation of 2D rodent brain MR images. The presented method focuses on detecting the topological changes of brain structures based on a novel area ratio criteria. The mean successful rate of the detection method shows 89.4% accuracy compared to the expert-identified ground truth.",

author = "Jinghao Zhou and Sukmoon Chang and Shaoting Zhang and George Pappas and Michael Michaelides and Foteini Delis and Nora Volkow and Panayotis Thanos and {Dimitris Metaxas}, Metaxas",

year = "2009",

doi = "10.1109/ISBI.2009.5193093",

language = "English (US)",

isbn = "9781424439324",

series = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009",

pages = "498--501",

booktitle = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging",

note = "2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 ; Conference date: 28-06-2009 Through 01-07-2009",

}

Zhou, J, Chang, S, Zhang, S, Pappas, G, Michaelides, M, Delis, F, Volkow, N, Thanos, P & Dimitris Metaxas, M 2009, Segmentation of rodent brains from MRI based on a novel statistical structure prediction method. in Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009., 5193093, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 498-501, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, Boston, MA, United States, 6/28/09. https://doi.org/10.1109/ISBI.2009.5193093

Segmentation of rodent brains from MRI based on a novel statistical structure prediction method. / Zhou, Jinghao; Chang, Sukmoon; Zhang, Shaoting et al.
Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 498-501 5193093 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Zhou, Jinghao

AU - Chang, Sukmoon

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AU - Michaelides, Michael

AU - Delis, Foteini

AU - Volkow, Nora

AU - Thanos, Panayotis

AU - Dimitris Metaxas, Metaxas

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AB - Functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric 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 neurodegeneration. Although various segmentation methods in clinical studies have been proposed, most of them require a priori knowledge about the locations of the structures of interest, preventing the fully automatic segmentation. 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 functional segmentation of 2D rodent brain MR images. The presented method focuses on detecting the topological changes of brain structures based on a novel area ratio criteria. The mean successful rate of the detection method shows 89.4% accuracy compared to the expert-identified ground truth.

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Zhou J, Chang S, Zhang S, Pappas G, Michaelides M, Delis F et al. Segmentation of rodent brains from MRI based on a novel statistical structure prediction method. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 498-501. 5193093. (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). doi: 10.1109/ISBI.2009.5193093

Segmentation of rodent brains from MRI based on a novel statistical structure prediction method

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