A novel learning based segmentation method for rodent brain structures using MRI

Jinghao Zhou; Sukmoon Chang; Qingshan Liu; George Pappas; Vasilios Boronikolas; Michael Michaelides; Nora D. Volkow; Panayotis K. Thanos; Dimitris Metaxas

doi:10.1109/ISBI.2008.4540932

A novel learning based segmentation method for rodent brain structures using MRI

Jinghao Zhou, Sukmoon Chang, Qingshan Liu, George Pappas, Vasilios Boronikolas, Michael Michaelides, Nora D. Volkow, Panayotis K. Thanos, Dimitris Metaxas

School of Science, Engineering & Technology (Harrisburg)

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

5 Scopus citations

Abstract

This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.

Original language	English (US)
Title of host publication	2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, Proceedings, ISBI
Pages	61-64
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2008.4540932
State	Published - 2008
Event	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France Duration: May 14 2008 → May 17 2008

Publication series

Name	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Other

Other	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Country/Territory	France
City	Paris
Period	5/14/08 → 5/17/08

All Science Journal Classification (ASJC) codes

Biomedical Engineering

Access to Document

10.1109/ISBI.2008.4540932

Cite this

Zhou, J., Chang, S., Liu, Q., Pappas, G., Boronikolas, V., Michaelides, M., Volkow, N. D., Thanos, P. K., & Metaxas, D. (2008). A novel learning based segmentation method for rodent brain structures using MRI. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI (pp. 61-64). Article 4540932 (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). https://doi.org/10.1109/ISBI.2008.4540932

@inproceedings{cf390ecb6a634e05968b61ad2b9e10a4,

title = "A novel learning based segmentation method for rodent brain structures using MRI",

abstract = "This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.",

author = "Jinghao Zhou and Sukmoon Chang and Qingshan Liu and George Pappas and Vasilios Boronikolas and Michael Michaelides and Volkow, {Nora D.} and Thanos, {Panayotis K.} and Dimitris Metaxas",

year = "2008",

doi = "10.1109/ISBI.2008.4540932",

language = "English (US)",

isbn = "9781424420032",

series = "2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI",

pages = "61--64",

booktitle = "2008 5th IEEE International Symposium on Biomedical Imaging",

note = "2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI ; Conference date: 14-05-2008 Through 17-05-2008",

}

Zhou, J, Chang, S, Liu, Q, Pappas, G, Boronikolas, V, Michaelides, M, Volkow, ND, Thanos, PK & Metaxas, D 2008, A novel learning based segmentation method for rodent brain structures using MRI. in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI., 4540932, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, pp. 61-64, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI, Paris, France, 5/14/08. https://doi.org/10.1109/ISBI.2008.4540932

A novel learning based segmentation method for rodent brain structures using MRI. / Zhou, Jinghao; Chang, Sukmoon; Liu, Qingshan et al.
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI. 2008. p. 61-64 4540932 (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI).

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

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AU - Zhou, Jinghao

AU - Chang, Sukmoon

AU - Liu, Qingshan

AU - Pappas, George

AU - Boronikolas, Vasilios

AU - Michaelides, Michael

AU - Volkow, Nora D.

AU - Thanos, Panayotis K.

AU - Metaxas, Dimitris

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N2 - This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.

AB - This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.

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Zhou J, Chang S, Liu Q, Pappas G, Boronikolas V, Michaelides M et al. A novel learning based segmentation method for rodent brain structures using MRI. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI. 2008. p. 61-64. 4540932. (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). doi: 10.1109/ISBI.2008.4540932

A novel learning based segmentation method for rodent brain structures using MRI

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