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

• Published in: Medical physics (Lancaster) Vol. 34; no. 2; pp. 722 - 736
• Main Authors: Montgomery, David W. G., Amira, Abbes, Zaidi, Habib
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.02.2007
• Subjects: ALGORITHMS; Algorithms for functional approximation; Artificial Intelligence; Biomedical modeling; cancer; CHEST; Cluster analysis; Computer Simulation; Data Interpretation, Statistical; DIAGNOSIS; Diseases; ERRORS; expectation‐maximisation algorithm; Gaussian mixture modeling; Humans; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; IMAGE PROCESSING; image segmentation; Imaging, Three-Dimensional - methods; MARKOV PROCESS; Markov processes; medical image processing; medical image segmentation; Medical imaging; Models, Biological; Models, Statistical; multiscale Markov modeling; Multiscale methods; NEOPLASMS; Neoplasms - diagnostic imaging; PATIENTS; pattern clustering; Pattern Recognition, Automated - methods; PHANTOMS; Phantoms, Imaging; PLANNING; POSITRON COMPUTED TOMOGRAPHY; positron emission tomography; Positron emission tomography (PET); Positron-Emission Tomography - instrumentation; Positron-Emission Tomography - methods; radiation therapy; RADIOLOGY AND NUCLEAR MEDICINE; RADIOTHERAPY; Reproducibility of Results; Sensitivity and Specificity; SIMULATION; Spatial analysis; STATISTICAL MODELS; tumours; wavelet; Wavelets; wavelet; Gaussian mixture modeling; positron emission tomography; medical image segmentation; multiscale Markov modeling
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.2432404

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.