ABSTRACT
Objective To delineate the normal stomach and thoracic stomach structure of patients with thoracic and abdominal tumor automatically using the AccuContour software based on deep learning in order to evaluate and compare the results. Methods Thirty-six patients with choracic and abdominal tumors were chosen for this study, and were divided into two groups. Group A included 18 patients with normal stomach, and group B included the other 18 patients undergoing esophageal carcinoma operation with thoracic stomach. The stomach structures were automatically delineated by the AccuContour software in the simulation CT series. Statistical analysis was carried out to data of the differences in volume, position and shape between the automatic and manual delineations, and data of the two kinds of stomach were compared. Results For group A, the differences in volume (ΔV%) between the automatic and manual delineations was (−1.82 ± 9.65)%, the total position difference (ΔL) was (0.51 ± 0.37) cm, the values of dice similarity coefficient (DSC) was 0.89 ± 0.04. There were significant differences in values of ΔV%、ΔL and DSC (P < 0.05). Conclusion The used version of AccuContour software in this study had a satisfactory result of automatic delineation of the normal stomach structure larger than certain volume, but could not delineate the thoracic stomach structures effectively for patients undergoing esophageal carcinoma operation.
ABSTRACT
Objective To evaluate the constancy of CT numbers of SIEMENS Sensation Open CT-simulator by analyzing the CT numbers of seven materials obtained from quality assurance(QA)tests. Methods QA tests for SIEMENS Sensation Open CT-simulator were performed with the Catphan504 phantom monthly. The CT images were obtained using three scan protocols(HeadSeq,RT_Head,and RT_Abdomen)for the CTP404 module in the phantom. The DoseLab software was used to analyze the 72 CT images acquired from January 2014 to December 2015,and the CT numbers(Y)of seven materials were obtained. Statistical analysis was performed on the Y data. The mean,standard deviation,maximum, minimum,and range values of Y for seven materials were calculated in three scan protocols. Results The standard deviation values of air,polymethylpentene,low-density polyethylene,polystyrene,acrylic acid, polyoxymethylene resin(Delrin),and polytetrafluoroethylene(Teflon)were as follows:(1)HeadSeq:0.54, 0.60,0.82,0.58,0.75,0.66,and 1.83 HU;(2)RT_Head:0.08,0.69,0.86,0.66,0.80,0.89,and 2.49 HU;(3)RT_Abdomen:0.11,0.61,0.76,0.72,0.78,0.96,and 2.56 HU.According to the statistical data, the constancy of CT numbers of the SIEMENS Sensation Open CT-simulator was in good condition in two years. Conclusions The variation of CT numbers of Teflon is the biggest among the seven materials. The relative values of CT numbers between different scan protocols vary with the relative electron density of materials.