@inproceedings{2aa8bc56f3894c82839245152566e3b2,
title = "Rule-based ventral cavity multi-organ automatic segmentation in CT scans",
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.",
author = "Spanier, {Assaf B.} and Leo Joskowicz",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 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 ; Conference date: 18-09-2014 Through 18-09-2014",
year = "2014",
doi = "10.1007/978-3-319-13972-2_15",
language = "الإنجليزيّة",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "163--170",
editor = "Henning M{\"u}ller and Bjoern Menze and Shaoting Zhang and Cai, {Weidong (Tom)} and Georg Langs and Dimitris Metaxas and Michael Kelm and Albert Montillo",
booktitle = "Medical Computer Vision",
address = "ألمانيا",
}