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

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 (D_eff ^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 D_eff ^corr 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 D_eff ^corr values. Then, 108 thoracic CT images and generated segmentation masks were used for network training. The water-equivalent diameter (D_w ) 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 SSDE_Deff ^{corr(automated)} , SSDE_Deff ^corr(manual) , and SSDE_Dw .

High agreement was obtained between the manual and automated methods for calculating the D_eff ^corr SSDE. The mean values for the SSDE_Deff ^corr(manual) , SSDE_Dw , and SSDE_Deff ^{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 D_eff ^corr SSDE in thoracic CT.