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Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model
Author(s) -
Montgomery David W. G.,
Amira Abbes,
Zaidi Habib
Publication year - 2007
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2432404
Subject(s) - voxel , segmentation , computer science , cardiac pet , markov random field , positron emission tomography , artificial intelligence , image segmentation , imaging phantom , expectation–maximization algorithm , medical imaging , initialization , cluster analysis , medical physics , pattern recognition (psychology) , computer vision , nuclear medicine , mathematics , statistics , medicine , maximum likelihood , programming language
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.