학술논문

Quantitative comparison of automated PET volume delineation methodologies using simulated tumor lesions
Document Type
Conference
Source
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on. :653-656 Mar, 2011
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Robotics and Control Systems
Lesions
Image reconstruction
Positron emission tomography
Image segmentation
Asynchronous transfer mode
Noise
Positron Emission Tomography
Tumor Delineation
Simulated Tumor Lesions
Gradient Segmentation
Stochastic Segmentation
Language
ISSN
1945-7928
1945-8452
Abstract
Robust tumor activity quantification recently finds application in challenging medical scenarios like early therapy response detection, radiotherapy treatment planning, etc. This paper targets a quantitative comparison of existing state of the art Positron Emission Tomography (PET) volume delineation methodologies. The different methods evaluated include adaptive threshold based, gradient based and stochastic image segmentations. For that purpose, spherical and non-spherical tumor lesions were simulated and studied. PET images were reconstructed with Maximum Likelihood Expectation-Maximization (MLEM) and Maximum A Posteriori (MAP) algorithms. All schemes were evaluated with reference to the ground truth knowledge. The spherical lesions were best segmented with the adaptive threshold based method, whereas the stochastic methods were slightly better for the non-spherical lesion.