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

PURPOSE: 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.

METHOD: We automatically classified specific slices from CT volume data using two different CNN architectures: VGG-16 and Xception. We organized the dataset into 5 categories corresponding to the contents of the specific slices. We also tested a 9-category version in which the slices were supplemented with their adjacent slices. We automatically obtained the imaging conditions from the DICOM tags of the specific slices that were classified from the CT volume data by the CNN and then estimated the effective exposure dose provided by the Web-based exposure estimation system.

RESULT: Using the 5-category dataset approach, the error in the effective exposure dose was 13% for VGG16 and 6% for Xception. When the 9-category approach was used, the error in the effective exposure dose was 0.8% for VGG16 and 0.6% for Xception. In both the architectures, less than 5 minutes was needed in the classification of the specific slices, followed by the extraction of their imaging conditions; however, VGG16 required the shortest processing time.

CONCLUSION: By supplementing a Web-based exposure estimation system with a CNN and adopting our proposed method, we were able to improve the efficiency of effective dose estimation.

Medienart:	E-Artikel

Erscheinungsjahr:	2020
Erschienen:	2020

Enthalten in:	Zur Gesamtaufnahme - volume:76
Enthalten in:	Nihon Hoshasen Gijutsu Gakkai zasshi - 76(2020), 11 vom: 26., Seite 1107-1117

Sprache:	Japanisch

Beteiligte Personen:	Esaki, Toru [VerfasserIn] Kawashima, Tomoaki [VerfasserIn]

Links:	Volltext

Themen:	Classification Computed tomography Deep convolutional neural network Effective dose Journal Article

Anmerkungen:	Date Completed 25.11.2020 Date Revised 25.11.2020 published: Print Citation Status MEDLINE

doi:	10.6009/jjrt.2020_JSRT_76.11.1107

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM317967584