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

Autor: Ruenjit S; Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.; Division of Diagnostic Radiology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.; Chulalongkorn University Biomedical Imaging Group, Depertment of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Siricharoen P; The Perceptual Intelligent Computing Lab, Department of Computer Engineering, Faculty of, Engineering, Chulalongkorn University, Bangkok, Thailand., Khamwan K; Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.; Chulalongkorn University Biomedical Imaging Group, Depertment of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.; Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Jazyk: angličtina
Zdroj: Journal of applied clinical medical physics [J Appl Clin Med Phys] 2024 Mar; Vol. 25 (3), pp. e14283. Date of Electronic Publication: 2024 Jan 31.
DOI: 10.1002/acm2.14283
Abstrakt: Purpose: 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).
Methods: 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 .
Results: 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.
Conclusion: The proposed automated framework using a CNN offers a reliable and efficient solution for determining the D eff corr SSDE in thoracic CT.
(© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)
Databáze: MEDLINE
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