Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours

Alexander Wood; S M Reza Soroushmehr; Negar Farzaneh; David Fessell; Kevin R Ward; Jonathan Gryak; Delaram Kahrobaei; Kayvan Na

doi:10.1109/EMBC.2018.8512182

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours

Alexander Wood, S M Reza Soroushmehr, Negar Farzaneh, David Fessell, Kevin R Ward, Jonathan Gryak, Delaram Kahrobaei, Kayvan Na

Computer Science

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

Abstract

Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.

Original language	English
Title of host publication	2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher	IEEE
Pages	53-56
Number of pages	4
Volume	2018
DOIs	https://doi.org/10.1109/EMBC.2018.8512182
Publication status	Published - Jul 2018

Publication series

Name	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
ISSN (Print)	1557-170X

Access to Document

10.1109/EMBC.2018.8512182

Cite this

Wood, A., Soroushmehr, S. M. R., Farzaneh, N., Fessell, D., Ward, K. R., Gryak, J., Kahrobaei, D., & Na, K. (2018). Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Vol. 2018, pp. 53-56). (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference). IEEE. https://doi.org/10.1109/EMBC.2018.8512182

Wood, Alexander ; Soroushmehr, S M Reza ; Farzaneh, Negar et al. / Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2018 IEEE, 2018. pp. 53-56 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference).

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title = "Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours",

abstract = "Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.",

author = "Alexander Wood and Soroushmehr, {S M Reza} and Negar Farzaneh and David Fessell and Ward, {Kevin R} and Jonathan Gryak and Delaram Kahrobaei and Kayvan Na",

year = "2018",

month = jul,

doi = "10.1109/EMBC.2018.8512182",

language = "English",

volume = "2018",

series = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference",

publisher = "IEEE",

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booktitle = "2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",

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Wood, A, Soroushmehr, SMR, Farzaneh, N, Fessell, D, Ward, KR, Gryak, J, Kahrobaei, D & Na, K 2018, Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). vol. 2018, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, IEEE, pp. 53-56. https://doi.org/10.1109/EMBC.2018.8512182

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours. / Wood, Alexander; Soroushmehr, S M Reza; Farzaneh, Negar et al.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2018 IEEE, 2018. p. 53-56 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference).

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

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N2 - Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.

AB - Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.

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Wood A, Soroushmehr SMR, Farzaneh N, Fessell D, Ward KR, Gryak J et al. Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2018. IEEE. 2018. p. 53-56. (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference). doi: 10.1109/EMBC.2018.8512182