Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

Abstract

The multi-slice computerized tomography (MSCT) is a medical Background: 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.This approach consists of three stages. The initial stage, an image Methods: 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.A total of 8 datasets available for patients diagnosed, from the cancer Results: 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.The segmentations obtained are useful for assessing Conclusions:qualitatively and quantitatively the stomach type 2 cancer. In addition, this type

3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor

“…With greater dense resolution, it is possible to recognize the single gastric wall layers as well as to estimate the invasive depth of the neoplasm on CT images ( 2 ), which is essential for tumor staging ( 3 ) and edge delineation. As a clear, accurate boundary is also of great importance in volume assessment ( 4 ), further radiomics feature analysis ( 5 ) and image-guided navigation ( 6 ), the precise CT-based tumor segmentation is quite desirable. However, the outlining process used to be completed manually on multi-slice images, which is quite subjective, labor-consuming and time-costing.…”

Three-Dimensional Reconstruction Algorithm for CT Pulmonary Angiography in Patients with Pulmonary Embolism Combined with Syncope

“…CT examination has become an indispensable way in the medical field of lung diagnosis [5]. With the continuous update of the 3D cone-beam reconstruction algorithm, the MC algorithm proposed by Chacón et al [6] is relatively prominent, although it can only get good results when the reconstruction cone angle is small. However, it has the advantages of simple operation, effective method, and fast effect, so the MC algorithm has always been the main algorithm used in actual projects.…”