Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

Ulas Bagci*, Jayaram K. Udupa, Neil Mendhiratta, Brent Foster, Ziyue Xu, Jianhua Yao, Xinjian Chen, Daniel J. Mollura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

123 Scopus citations

Abstract

We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.

Original languageEnglish (US)
Pages (from-to)929-945
Number of pages17
JournalMedical Image Analysis
Volume17
Issue number8
DOIs
StatePublished - Dec 2013
Externally publishedYes

Keywords

  • MRI-PET Co-segmentation
  • PET segmentation
  • PET-CT Co-segmentation
  • Random Walk
  • Simultaneous segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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