Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices

Liyuan Chen, Chenyang Shen, Zhiguo Zhou, Genevieve Maquilan, Kimberly Thomas, Michael R. Folkert, Kevin Albuquerque, Jing Wang

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Because in PET imaging cervical tumors are close to the bladder with high capacity for the secreted 18FDG tracer, conventional intensity-based segmentation methods often misclassify the bladder as a tumor. Based on the observation that tumor position and area do not change dramatically from slice to slice, we propose a two-stage scheme that facilitates segmentation. In the first stage, we used a graph-cut based algorithm to obtain initial contouring of the tumor based on local similarity information between voxels; this was achieved through manual contouring of the cervical tumor on one slice. In the second stage, initial tumor contours were fine-tuned to more accurate segmentation by incorporating similarity information on tumor shape and position among adjacent slices, according to an intensity-spatial-distance map. Experimental results illustrate that the proposed two-stage algorithm provides a more effective approach to segmenting cervical tumors in 3D18FDG PET images than the benchmarks used for comparison.

Original languageEnglish (US)
Pages (from-to)30-36
Number of pages7
JournalComputers in Biology and Medicine
Volume97
DOIs
StatePublished - Jun 1 2018

Keywords

  • Cervical PET
  • Graph-cut
  • Similarity-based variational model
  • Tumor segmentation

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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