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23 March 2019 Model and human observer reproducibility for detection of microcalcification clusters in digital breast tomosynthesis images of three-dimensionally structured test object
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Abstract
We compare the reproducibility of the human observers and a channelized Hotelling observer (CHO), when reading digital breast tomosynthesis (DBT) images of a physical phantom containing a breast simulating structured background and calcification clusters at three dose levels. The phantom is scanned 217 times on a Siemens Inspiration DBT system. Volumes of interest, with and without the calcification targets, are extracted and the human observers’ percentage of correct (PC) scores is evaluated using a four-alternative forced choice method. A two-layer CHO is developed using the human observer results. The first layer consists of a localizing CHO that identifies the most conspicuous calcifications using two Laguerre–Gauss channels. Then a CHO with eight Gabor channels estimates the PC score for the calcification cluster. Observer reproducibility is estimated by bootstrapping, and the standard deviation (SD) is used as a figure of merit. The CHO closely approximated the human observer results for all the three dose levels with a correlation of >0.97. For the larger calcification cluster sizes, both observers have similar reproducibility, whereas the CHO is more reproducible for the smaller calcifications, with a maximum of 5.5 SD against 13.1 SD for the human observers. The developed CHO is a good candidate for automated reading of the calcification clusters of the structured phantom, with better reproducibility than the human readers for small calcifications.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$25.00 © 2019 SPIE
Dimitar Petrov, Nicholas Marshall, Kenneth Young, Guozhi Zhang, and Hilde Bosmans "Model and human observer reproducibility for detection of microcalcification clusters in digital breast tomosynthesis images of three-dimensionally structured test object," Journal of Medical Imaging 6(1), 015503 (23 March 2019). https://doi.org/10.1117/1.JMI.6.1.015503
Received: 6 August 2018; Accepted: 5 March 2019; Published: 23 March 2019
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