Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices. (1st June 2018)

• Record Type: Journal Article
• Authors: Chen, Liyuan; Shen, Chenyang; Zhou, Zhiguo; Maquilan, Genevieve; Thomas, Kimberly; Folkert, Michael R.; Albuquerque, Kevin; Wang, Jing
• Is Part Of: Computers in biology and medicine. Volume 97(2018)
• Page Start: 30
• Page End: 36
• Publication Date: 2018-06-01
• Subjects: Cervical PET -- Tumor segmentation -- Graph-cut -- Similarity-based variational model; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.04.009
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Because in PET imaging cervical tumors are close to the bladder with high capacity for the secreted 18 FDG 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 3D 18 FDG PET images than the benchmarks used for comparison. Highlights: Proximity between cervix and bladder makes PET segmentation challenging. Similarity among adjacent slices in tumor shape and position improves segmentation. Proposed two-stage method improves dice coefficient from one-stage methods. Proposed two-stage method outperforms other commonly used segmentation methods.