Fuzzy Statistical Unsupervised Learning Based Total Lesion Metabolic Activity Estimation in Positron Emission Tomography Images
Accurate tumor lesion activity estimation is critical for tumor staging and follow up studies. Positron emission tomography (PET) successfully images and quantifies the lesion metabolic activity. Recently, PET images were modeled as a fuzzy Gaussian mixture to delineate tumor lesions accurately. Nonetheless, on the course of accurate delineation, chances are high to potentially end up with activity underestimation, due to the limited PET resolution, the reconstruction images suffer from partial volume effects (PVE). In this work, we propose a statistical lesion activity computation (SLAC) approach to robustly estimate the total lesion activity (TLA) directly from the modeled Gaussian partial volume mixtures. To evaluate the proposed method, synthetic lesions were simulated and reconstructed. TLA was estimated from 3 state-of-the-art PET delineation schemes for comparison. All schemes were evaluated with reference to the ground truth knowledge. The experimental results convey that the SLAC is robust enough for clinical use.
KeywordsPositron emission tomography tumor activity estimation finite mixture models Gaussian distribution partial volume modeling linear combination (LC) of random variables
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