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

Abstract

This 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 (Deff corr ) in thoracic computed tomography (CT). Transaxial images obtained from 108 adult patients who underwent non-contrast thoracic CT scans were analyzed. To calculate the Deff corr according to Mihailidis etal., 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 Deff corr 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 SSDEDeff corr(automated) , SSDEDeff corr(manual) , and SSDEDw . High agreement was obtained between the manual and automated methods for calculating the Deff corr SSDE. The mean values for the SSDEDeff corr(manual) , SSDEDw , and SSDEDeff corr(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. The proposed automated framework using a CNN offers a reliable and efficient solution for determining the Deff corr SSDE in thoracic CT.