Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.

Journal: European journal of radiology

Published Date: Jan 8, 2021

Abstract

PURPOSE: To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system.

Authors

Stephan P Blazis

Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands. Electronic address: s.blazis@asz.nl.
Dennis B M Dickerscheid

Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.
Philip V M Linsen

Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.
Carine O Martins Jarnalo

Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.

Keywords

Algorithms Deep Learning Humans Lung Lung Neoplasms Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Sensitivity and Specificity Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (33453573)

Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.

Abstract

Authors

Keywords

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