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Web-based exposure estimation systems are advantageous for estimating exposure doses for computed tomography (CT) scans. However, such systems depend on the imaging conditions of the slices, and a considerable amount of time and effort is needed to select the slices and extract their imaging conditions from the relevant CT volume data. In this study, we used a convolutional neural network (CNN) to automatically classify specific slices from available CT volume data for use by a Web-based exposure estimation system. We also proposed a method to automatically obtain the imaging conditions of these classified slices. The objective of this study was to improve the efficiency of effective dose estimation.

nippon hoshasen gijutsu gakkai zasshi/nihon houshasen gijutsu gakkai zasshi

Supplementing a Web-based Exposure Estimation System with Deep Learning for Automatic Classification of CT Images to Increase the Efficiency of Effective Dose Estimation