Segmentação de imagens PET para definição de volumes de tratamento em planejamento radioterápico
DOI:
https://doi.org/10.29384/rbfm.2023.v17.19849001606Keywords:
Cancer, Radiotherapy, Image processing, Positron Emission TomographyAbstract
According to the statistical projections of the Brazilian National Cancer Institute, the estimated incidence of cancer in Brazil is approximately 626,030 new cases in each year of 2020-2022. One of the main forms of cancer treatment is radiation therapy. The most suitable way to irradiate the tumor volume is determined on the radiotherapy planning, while minimizing damage to surrounding healthy tissue. The definition of the target volume is usually performed by manual delineation directly on a radiological image of the patient by the radiotherapist. This leads to a higher level of uncertainty since this segmentation method is subjective. This work aims to collaborate in the identification of more adequate segmentation methods for defining volumes in PET images. Segmentation methods were performed in IEC/NEMA phantom images using the ImageJ software. The evaluated methods were threshold, level sets and k-means clustering. The method that showed the most satisfactory results was the k-means clustering, being the automatic method recommended for segmentation of PET images. However, more research is needed to develop techniques that define more precisely the target volume in radiotherapy planning, mainly due to the high noise level and low spatial resolution of PET images.
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