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Segmentation of PET Images for Computer-Aided Functional Quantification of Tuberculosis in Small Animal Models
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November 05 2013
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Source: IEEE Trans Biomed Eng. 2013; 61(3):711-724
[PDF-2.88 MB]
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Alternative Title:IEEE Trans Biomed Eng
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Personal Author:
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Description:Pulmonary infections often cause spatially diffuse and multi-focal radiotracer uptake in positron emission tomography (PET) images, which makes accurate quantification of the disease extent challenging. Image segmentation plays a vital role in quantifying uptake due to the distributed nature of immuno-pathology and associated metabolic activities in pulmonary infection, specifically tuberculosis (TB). For this task, thresholding-based segmentation methods may be better suited over other methods; however, performance of the thresholding-based methods depend on the selection of thresholding parameters, which are often suboptimal. Several optimal thresholding techniques have been proposed in the literature, but there is currently no consensus on how to determine the optimal threshold for precise identification of spatially diffuse and multi-focal radiotracer uptake. In this study, we propose a method to select optimal thresholding levels by utilizing a novel intensity affinity metric within the affinity propagation clustering framework. We tested the proposed method against 70 longitudinal PET images of rabbits infected with TB. The overall dice similarity coefficient between the segmentation from the proposed method and two expert segmentations was found to be 91.25 ±8.01% with a sensitivity of 88.80 ±12.59% and a specificity of 96.01 ±9.20%. High accuracy and heightened efficiency of our proposed method, as compared to other PET image segmentation methods, were reported with various quantification metrics.
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Pubmed ID:24235292
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Pubmed Central ID:PMC4196700
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Funding:R01AI079590/AI/NIAID NIH HHS/United States ; R01A1035272/PHS HHS/United States ; DP2 OD006492/ODCDC CDC HHS/Office of the Director/United States ; R01 AI079590/NIAID NIH HHS/National Institute of Allergy and Infectious Diseases Extramural Activities/United States ; ZIA CL090018-04/Intramural NIH HHS/Intramural NIH HHS/United States ; ... More +
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Volume:61
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Issue:3
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