Longitudinal assessment of brain tumors using a repeatable prior-based segmentation

L. Weizman*, L. Joskowicz, L. Ben-Sira, B. Shofty, S. Constantini, D. Ben-Bashat

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents an automatic method for a repeatable, prior-based segmentation and classification of brain tumors in longitudinal MR scans. The method is designed to overcome the inter/intra observer variability and to provide a repeatable delineation of the tumor boundaries in a set of follow-up scans of the same patient. The method effectively incorporates manual delineation of the first scan in the time-series to segment and classify a series of follow-up scans. Experimental results on 16 datasets yield a mean surface distance error of 0.22mm and a mean volume overlap difference of 12.34% as compared to manual segmentation by an expert radiologist.

Original languageAmerican English
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages1733-1736
Number of pages4
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period30/03/112/04/11

Keywords

  • MRI
  • brain tumor
  • follow-up
  • segmentation

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