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 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.

Reproducibility of 18F-FDG and 3′-Deoxy-3′-18F-Fluorothymidine PET Tumor Volume Measurements

“…In addition, this method depends on the background ROI choice, which can in turn lead to reduced interobserver reproducibility for functionalvolume determination. A few automatic algorithms have been proposed (16)(17)(18)(19). The main difference between these algorithms and the threshold-based approaches is that the algorithms automatically estimate the parameters of interest and find the optimal regions' characteristics in a given image, without system-dependent parameters.…”

New strategy for automatic tumor segmentation by adaptive thresholding on PET/CT images

“…However, these adaptive thresholds methods suffer from problems and inaccuracies when applied to real tumors instead of to phantoms. To resolve these problems, some authors have worked on improving and changing the phantom, in order to give it shape and characteristics that more closely match those of the patient (18) . Nevertheless, these adaptive thresholds methods still have reliability problems in clinical applications likely due to the phantom's inability to accurately represent the human body.…”

Rationale, Instrumental Accuracy, and Challenges of PET Quantification for Tumor Segmentation in Radiation Treatment Planning

“…It is known that the fixed and adaptive threshold methods are challenged by irregularly shaped non-uniform activity distributions (Black et al, 2004;Hatt, Cheze le Rest, van Baardwijk, et al, 2011). Finally, the advanced segmentation tools have been demonstrated to be more accurate and robust to non-uniform activity distributions, Montgomery et al, 2007;Li et al, 2008;Hatt, Cheze Le Rest, Albarghach, et al, www.intechopen.com…”