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Comput Biol Med. 2018 Jun 1;97:30-36. doi: 10.1016/j.compbiomed.2018.04.009. Epub 2018 Apr 16.

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

Author information

1
University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Rd., Dallas, TX, 75214, United States. Electronic address: liyuan.chen@utsouthwestern.edu.
2
University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Rd., Dallas, TX, 75214, United States.
3
University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Rd., Dallas, TX, 75214, United States. Electronic address: jing.wang@utsouthwestern.edu.

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.

KEYWORDS:

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

PMID:
29684783
PMCID:
PMC5970095
DOI:
10.1016/j.compbiomed.2018.04.009
[Indexed for MEDLINE]
Free PMC Article

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