A fully automated spinal cord segmentation

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

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

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 languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages524-528
Number of pages5
ISBN (Electronic)9781728112954
DOIs
Publication statusPublished - 20-02-2019
Externally publishedYes
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26-11-201829-11-2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period26-11-1829-11-18

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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). [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.",
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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 proceedingConference contribution

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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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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). https://doi.org/10.1109/GlobalSIP.2018.8646682