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

Assaf B. Spanier*, Leo Joskowicz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageAmerican English
Title of host publicationMedical Computer Vision
Subtitle of host publicationAlgorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers
EditorsHenning 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
PublisherSpringer Verlag
Pages163-170
Number of pages8
ISBN (Electronic)9783319139715
DOIs
StatePublished - 2014
EventInternational 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 201418 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8848
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational 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/TerritoryUnited States
CityCambridge
Period18/09/1418/09/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

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