Wavelets based Algorithm for the Evaluation of Enhanced Liver Areas
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Data
2014-01-01
Autores
Alvarez, Matheus [UNESP]
Pina, Diana Rodrigues de
Giacomini, Guilherme [UNESP]
Romeiro, Fernando Gomes
Duarte, Sergio Barbosa
Yamashita, Seizo
Arruda Miranda, Jose Ricardo de [UNESP]
Ourselin, S.
Styner, M. A.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Spie - Int Soc Optical Engineering
Resumo
Hepatocellular carcinoma (HCC) is a primary tumor of the liver. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures. Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0cm was found for small-sized tumors. Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small increase for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented with non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.
Descrição
Palavras-chave
HCC, medical image segmentation, liver, medical imaging, computed tomography, image processing
Como citar
Medical Imaging 2014: Image Processing. Bellingham: Spie-int Soc Optical Engineering, v. 9034, 9 p., 2014.