A fully automated spinal cord segmentation

S. P. Subramanya Jois; Harsha Sridhar; J. R. Harish Kumar

doi:10.1109/GlobalSIP.2018.8646682

A fully automated spinal cord segmentation

S. P. Subramanya Jois
, Harsha Sridhar
, J. R. Harish Kumar

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

4 Citations (Scopus)

Abstract

Segmentation of the spinal cord region is an imperative step in the automated analysis of neurological ailments such as multiple sclerosis. Multiple studies demonstrated the connection between progression of neurological diseases and measurements identifying with spinal cord atrophy and changes to its structure. Segmentation of spinal cord region manually or semi-automatically, can be conflicting and tedious for large datasets. We present a novel automated method, that segments the spinal cord region, utilizing circular active discs and region growth algorithm. The proposed method is validated on the Visible Human Project dataset. The results with regards to sensitivity, specificity, accuracy, Jaccard index, and Dice coefficient were 97.23%, 100%, 99.76%, 96.83%, and 98.65%, respectively. The results were observed to be highly precise in comparison to expert outlines.

Original language	English
Title of host publication	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	524-528
Number of pages	5
ISBN (Electronic)	9781728112954
DOIs	https://doi.org/10.1109/GlobalSIP.2018.8646682
Publication status	Published - 20-02-2019
Event	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States Duration: 26-11-2018 → 29-11-2018

Publication series

Name	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/Territory	United States
City	Anaheim
Period	26-11-18 → 29-11-18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Information Systems
Signal Processing

Access to Document

10.1109/GlobalSIP.2018.8646682

Cite this

Subramanya Jois, S. P., Sridhar, H., & Harish Kumar, J. R. (2019). A fully automated spinal cord segmentation. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 524-528). Article 8646682 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646682

Subramanya Jois, S. P. ; Sridhar, Harsha ; Harish Kumar, J. R. / A fully automated spinal cord segmentation. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 524-528 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

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title = "A fully automated spinal cord segmentation",

abstract = "Segmentation of the spinal cord region is an imperative step in the automated analysis of neurological ailments such as multiple sclerosis. Multiple studies demonstrated the connection between progression of neurological diseases and measurements identifying with spinal cord atrophy and changes to its structure. Segmentation of spinal cord region manually or semi-automatically, can be conflicting and tedious for large datasets. We present a novel automated method, that segments the spinal cord region, utilizing circular active discs and region growth algorithm. The proposed method is validated on the Visible Human Project dataset. The results with regards to sensitivity, specificity, accuracy, Jaccard index, and Dice coefficient were 97.23\%, 100\%, 99.76\%, 96.83\%, and 98.65\%, respectively. The results were observed to be highly precise in comparison to expert outlines.",

author = "\{Subramanya Jois\}, \{S. P.\} and Harsha Sridhar and \{Harish Kumar\}, \{J. R.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",

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series = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",

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Subramanya Jois, SP, Sridhar, H & Harish Kumar, JR 2019, A fully automated spinal cord segmentation. in 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings., 8646682, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 524-528, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, Anaheim, United States, 26-11-18. https://doi.org/10.1109/GlobalSIP.2018.8646682

A fully automated spinal cord segmentation. / Subramanya Jois, S. P.; Sridhar, Harsha; Harish Kumar, J. R.
2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 524-528 8646682 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

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

TY - GEN

T1 - A fully automated spinal cord segmentation

AU - Subramanya Jois, S. P.

AU - Sridhar, Harsha

AU - Harish Kumar, J. R.

PY - 2019/2/20

Y1 - 2019/2/20

N2 - Segmentation of the spinal cord region is an imperative step in the automated analysis of neurological ailments such as multiple sclerosis. Multiple studies demonstrated the connection between progression of neurological diseases and measurements identifying with spinal cord atrophy and changes to its structure. Segmentation of spinal cord region manually or semi-automatically, can be conflicting and tedious for large datasets. We present a novel automated method, that segments the spinal cord region, utilizing circular active discs and region growth algorithm. The proposed method is validated on the Visible Human Project dataset. The results with regards to sensitivity, specificity, accuracy, Jaccard index, and Dice coefficient were 97.23%, 100%, 99.76%, 96.83%, and 98.65%, respectively. The results were observed to be highly precise in comparison to expert outlines.

AB - Segmentation of the spinal cord region is an imperative step in the automated analysis of neurological ailments such as multiple sclerosis. Multiple studies demonstrated the connection between progression of neurological diseases and measurements identifying with spinal cord atrophy and changes to its structure. Segmentation of spinal cord region manually or semi-automatically, can be conflicting and tedious for large datasets. We present a novel automated method, that segments the spinal cord region, utilizing circular active discs and region growth algorithm. The proposed method is validated on the Visible Human Project dataset. The results with regards to sensitivity, specificity, accuracy, Jaccard index, and Dice coefficient were 97.23%, 100%, 99.76%, 96.83%, and 98.65%, respectively. The results were observed to be highly precise in comparison to expert outlines.

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DO - 10.1109/GlobalSIP.2018.8646682

M3 - Conference contribution

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T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

SP - 524

EP - 528

BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018

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ER -

Subramanya Jois SP, Sridhar H, Harish Kumar JR. A fully automated spinal cord segmentation. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 524-528. 8646682. (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). doi: 10.1109/GlobalSIP.2018.8646682

A fully automated spinal cord segmentation

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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