Automated size‐specific dose estimates framework in thoracic CT using convolutional neural network based on U‐Net model

Abstract

AbstractPurposeThis study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size‐specific dose estimates (SSDEs) based on the corrected effective diameter (Deffcorr) in thoracic computed tomography (CT).MethodsTransaxial images obtained from 108 adult patients who underwent non‐contrast thoracic CT scans were analyzed. To calculate the Deffcorr according to Mihailidis et al., the average relative electron densities for lung, bone, and other tissues were used to correct the lateral and anterior–posterior dimensions. The CNN architecture based on the U‐Net algorithm was used for automated segmentation of three classes of tissues and the background region to calculate dimensions and Deffcorr values. Then, 108 thoracic CT images and generated segmentation masks were used for network training. The water‐equivalent diameter (Dw) was determined according to the American Association of Physicists in Medicine Task Group 220. Linear regression and Bland–Altman analysis were performed to determine the correlations between SSDEDeffcorr(automated), SSDEDeffcorr(manual), and SSDEDw.ResultsHigh agreement was obtained between the manual and automated methods for calculating the Deffcorr SSDE. The mean values for the SSDEDeffcorr(manual), SSDEDw, and SSDEDeffcorr(automated) were 14.3 ± 2.1 mGy, 14.6 ± 2.2 mGy, and 14.5 ± 2.4 mGy, respectively. The U‐Net model was successfully trained and used to accurately predict SSDEs, with results comparable to manual‐labeling results.ConclusionThe proposed automated framework using a CNN offers a reliable and efficient solution for determining the Deffcorr SSDE in thoracic CT.