Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer [version 2; referees: 1 approved, 1 not approved]
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Fecha
2018-07
Autores
Chacón, Gerardo
Rodríguez, Johel E.
Bermúdez, Valmore
Vera, Miguel
Hernández, Juan Diego
Vargas, Sandra
Pardo, Aldo
Lameda, Carlos
Madriz, Delia
Bravo, Antonio J.
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F1000 Research Ltda.
Resumen
Background: The multi–slice computerized tomography (MSCT) is a medical
imaging modality that has been used to determine the size and location of the
stomach cancer. Additionally, MSCT is considered the best modality for the
staging of gastric cancer. One way to assess the type 2 cancer of stomach is by
detecting the pathological structure with an image segmentation approach. The
tumor segmentation of MSCT gastric cancer images enables the diagnosis of
the disease condition, for a given patient, without using an invasive method as
surgical intervention.
Methods: This approach consists of three stages. The initial stage, an image
enhancement, consists of a method for correcting non homogeneities present
in the background of MSCT images. Then, a segmentation stage using a
clustering method allows to obtain the adenocarcinoma morphology. In the
third stage, the pathology region is reconstructed and then visualized with a
three–dimensional (3–D) computer graphics procedure based on marching
cubes algorithm. In order to validate the segmentations, the Dice score is used
as a metric function useful for comparing the segmentations obtained using the
proposed method with respect to ground truth volumes traced by a clinician.
Results: A total of 8 datasets available for patients diagnosed, from the cancer
data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma
(TCGASTAD) is considered in this research. The volume of the type 2 stomach
tumor is estimated from the 3–D shape computationally segmented from the
each dataset. These 3–D shapes are computationally reconstructed and then
used to assess the morphopathology macroscopic features of this cancer.
Conclusions: The segmentations obtained are useful for assessing
qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the
planning of virtual surgical processes related to type 2 cancer.
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Palabras clave
Stomach tumor, Type 2 cancer, Medical imaging, Multi–slice computerized tomography, Image enhancement, Region growing method, Marching cubes, Three-dimensional reconstruction