Rule-based ventral cavity multi-organ automatic segmentation in CT scans

Assaf B. Spanier; Leo Joskowicz

doi:10.1007/978-3-319-13972-2_15

Rule-based ventral cavity multi-organ automatic segmentation in CT scans

Assaf B. Spanier^*, Leo Joskowicz

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

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

8 Scopus citations

Abstract

We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented. First, the air-containing organs are segmented: the trachea and the lungs. Then, the organs with high blood content: the spleen, the kidneys and the liver, are segmented. Each organ is individually segmented with a generic four-step pipeline procedure. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 20 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield an average Dice volume overlap similarity score of 90.95. For the 10 CT scans test datasets, the average Dice scores is 88.5.

Original language	English
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers
Editors	Henning Müller, Bjoern Menze, Shaoting Zhang, Weidong (Tom) Cai, Bjoern Menze, Georg Langs, Dimitris Metaxas, Georg Langs, Henning Müller, Michael Kelm, Albert Montillo, Weidong (Tom) Cai
Publisher	Springer Verlag
Pages	163-170
Number of pages	8
ISBN (Electronic)	9783319139715
DOIs	https://doi.org/10.1007/978-3-319-13972-2_15
State	Published - 2014
Event	International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014 - Cambridge, United States Duration: 18 Sep 2014 → 18 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8848
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014
Country/Territory	United States
City	Cambridge
Period	18/09/14 → 18/09/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

Access to Document

10.1007/978-3-319-13972-2_15

Cite this

Spanier, A. B., & Joskowicz, L. (2014). Rule-based ventral cavity multi-organ automatic segmentation in CT scans. In H. Müller, B. Menze, S. Zhang, W. Cai, B. Menze, G. Langs, D. Metaxas, G. Langs, H. Müller, M. Kelm, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers (pp. 163-170). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848). Springer Verlag. https://doi.org/10.1007/978-3-319-13972-2_15

Spanier, Assaf B. ; Joskowicz, Leo. / Rule-based ventral cavity multi-organ automatic segmentation in CT scans. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. editor / Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Weidong (Tom) Cai ; Bjoern Menze ; Georg Langs ; Dimitris Metaxas ; Georg Langs ; Henning Müller ; Michael Kelm ; Albert Montillo ; Weidong (Tom) Cai. Springer Verlag, 2014. pp. 163-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented. First, the air-containing organs are segmented: the trachea and the lungs. Then, the organs with high blood content: the spleen, the kidneys and the liver, are segmented. Each organ is individually segmented with a generic four-step pipeline procedure. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 20 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield an average Dice volume overlap similarity score of 90.95. For the 10 CT scans test datasets, the average Dice scores is 88.5.",

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Spanier, AB & Joskowicz, L 2014, Rule-based ventral cavity multi-organ automatic segmentation in CT scans. in H Müller, B Menze, S Zhang, W Cai, B Menze, G Langs, D Metaxas, G Langs, H Müller, M Kelm, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8848, Springer Verlag, pp. 163-170, International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014, Cambridge, United States, 18/09/14. https://doi.org/10.1007/978-3-319-13972-2_15

Rule-based ventral cavity multi-organ automatic segmentation in CT scans. / Spanier, Assaf B.; Joskowicz, Leo.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. ed. / Henning Müller; Bjoern Menze; Shaoting Zhang; Weidong (Tom) Cai; Bjoern Menze; Georg Langs; Dimitris Metaxas; Georg Langs; Henning Müller; Michael Kelm; Albert Montillo; Weidong (Tom) Cai. Springer Verlag, 2014. p. 163-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848).

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

TY - GEN

T1 - Rule-based ventral cavity multi-organ automatic segmentation in CT scans

AU - Spanier, Assaf B.

AU - Joskowicz, Leo

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N2 - We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented. First, the air-containing organs are segmented: the trachea and the lungs. Then, the organs with high blood content: the spleen, the kidneys and the liver, are segmented. Each organ is individually segmented with a generic four-step pipeline procedure. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 20 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield an average Dice volume overlap similarity score of 90.95. For the 10 CT scans test datasets, the average Dice scores is 88.5.

AB - We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented. First, the air-containing organs are segmented: the trachea and the lungs. Then, the organs with high blood content: the spleen, the kidneys and the liver, are segmented. Each organ is individually segmented with a generic four-step pipeline procedure. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 20 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield an average Dice volume overlap similarity score of 90.95. For the 10 CT scans test datasets, the average Dice scores is 88.5.

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A2 - Langs, Georg

A2 - Metaxas, Dimitris

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A2 - Montillo, Albert

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Spanier AB, Joskowicz L. Rule-based ventral cavity multi-organ automatic segmentation in CT scans. In Müller H, Menze B, Zhang S, Cai W, Menze B, Langs G, Metaxas D, Langs G, Müller H, Kelm M, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. Springer Verlag. 2014. p. 163-170. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-13972-2_15

Rule-based ventral cavity multi-organ automatic segmentation in CT scans

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