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Title: Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model

Abstract

The widespread application of positron emission tomography (PET) in clinical oncology has driven this imaging technology into a number of new research and clinical arenas. Increasing numbers of patient scans have led to an urgent need for efficient data handling and the development of new image analysis techniques to aid clinicians in the diagnosis of disease and planning of treatment. Automatic quantitative assessment of metabolic PET data is attractive and will certainly revolutionize the practice of functional imaging since it can lower variability across institutions and may enhance the consistency of image interpretation independent of reader experience. In this paper, a novel automated system for the segmentation of oncological PET data aiming at providing an accurate quantitative analysis tool is proposed. The initial step involves expectation maximization (EM)-based mixture modeling using a k-means clustering procedure, which varies voxel order for initialization. A multiscale Markov model is then used to refine this segmentation by modeling spatial correlations between neighboring image voxels. An experimental study using an anthropomorphic thorax phantom was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The comparison of actual tumor volumes to the volumes calculated using different segmentation methodologies including standard k-means, spatial domainmore » Markov Random Field Model (MRFM), and the new multiscale MRFM proposed in this paper showed that the latter dramatically reduces the relative error to less than 8% for small lesions (7 mm radii) and less than 3.5% for larger lesions (9 mm radii). The analysis of the resulting segmentations of clinical oncologic PET data seems to confirm that this methodology shows promise and can successfully segment patient lesions. For problematic images, this technique enables the identification of tumors situated very close to nearby high normal physiologic uptake. The use of this technique to estimate tumor volumes for assessment of response to therapy and to delineate treatment volumes for the purpose of combined PET/CT-based radiation therapy treatment planning is also discussed.« less

Authors:
; ;  [1];  [2];  [3]
  1. School of Electronics, Electrical Engineering and Computer Science, ECIT, The Queen's University of Belfast, Belfast, Northern Ireland (United Kingdom)
  2. (United Kingdom)
  3. (Switzerland)
Publication Date:
OSTI Identifier:
20951062
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 34; Journal Issue: 2; Other Information: DOI: 10.1118/1.2432404; (c) 2007 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; CHEST; DIAGNOSIS; ERRORS; IMAGE PROCESSING; IMAGES; MARKOV PROCESS; NEOPLASMS; PATIENTS; PHANTOMS; PLANNING; POSITRON COMPUTED TOMOGRAPHY; RADIOTHERAPY; SIMULATION; STATISTICAL MODELS

Citation Formats

Montgomery, David W. G., Amira, Abbes, Zaidi, Habib, School of Engineering and Design, Brunel University, London, Uxbridge, and Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva 4. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. United States: N. p., 2007. Web. doi:10.1118/1.2432404.
Montgomery, David W. G., Amira, Abbes, Zaidi, Habib, School of Engineering and Design, Brunel University, London, Uxbridge, & Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva 4. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. United States. doi:10.1118/1.2432404.
Montgomery, David W. G., Amira, Abbes, Zaidi, Habib, School of Engineering and Design, Brunel University, London, Uxbridge, and Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva 4. Thu . "Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model". United States. doi:10.1118/1.2432404.
@article{osti_20951062,
title = {Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model},
author = {Montgomery, David W. G. and Amira, Abbes and Zaidi, Habib and School of Engineering and Design, Brunel University, London, Uxbridge and Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva 4},
abstractNote = {The widespread application of positron emission tomography (PET) in clinical oncology has driven this imaging technology into a number of new research and clinical arenas. Increasing numbers of patient scans have led to an urgent need for efficient data handling and the development of new image analysis techniques to aid clinicians in the diagnosis of disease and planning of treatment. Automatic quantitative assessment of metabolic PET data is attractive and will certainly revolutionize the practice of functional imaging since it can lower variability across institutions and may enhance the consistency of image interpretation independent of reader experience. In this paper, a novel automated system for the segmentation of oncological PET data aiming at providing an accurate quantitative analysis tool is proposed. The initial step involves expectation maximization (EM)-based mixture modeling using a k-means clustering procedure, which varies voxel order for initialization. A multiscale Markov model is then used to refine this segmentation by modeling spatial correlations between neighboring image voxels. An experimental study using an anthropomorphic thorax phantom was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The comparison of actual tumor volumes to the volumes calculated using different segmentation methodologies including standard k-means, spatial domain Markov Random Field Model (MRFM), and the new multiscale MRFM proposed in this paper showed that the latter dramatically reduces the relative error to less than 8% for small lesions (7 mm radii) and less than 3.5% for larger lesions (9 mm radii). The analysis of the resulting segmentations of clinical oncologic PET data seems to confirm that this methodology shows promise and can successfully segment patient lesions. For problematic images, this technique enables the identification of tumors situated very close to nearby high normal physiologic uptake. The use of this technique to estimate tumor volumes for assessment of response to therapy and to delineate treatment volumes for the purpose of combined PET/CT-based radiation therapy treatment planning is also discussed.},
doi = {10.1118/1.2432404},
journal = {Medical Physics},
number = 2,
volume = 34,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2007},
month = {Thu Feb 15 00:00:00 EST 2007}
}