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Proceedings Paper

Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images
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Paper Abstract

Image quality assessment is important to maintain and improve the imaging system performance, and conducting a human observer study is considered the most desirable approach for the given task because the human makes the diagnostic decision. However, performing a human observer study is time-consuming and expensive. As an alternative method, mathematical model observers to mimic the human observer performance have been proposed. In this work, we proposed convolutional neural network (CNN) based anthropomorphic model observer and compared its performance with human observer and dense difference-of-Gaussian channelized Hotelling observers (D-DOG CHO) for breast tomosynthesis images. The proposed network contained input image, 2D convolution, batch normalization, leaky ReLU, fully connected, and regression layers, and we trained the network using stochastic gradient with momentum (SGDM) optimizer with design parameters, such as filter size and number of filters. For training, validation, and testing data set, anatomical background with 30% volume glandular fraction was generated using the power law spectrum of breast anatomy, and sphere object with a 1 mm diameter was used as a lesion for detection task. In-plane breast tomosynthesis images were obtained using filtered back-projection based tomosynthesis reconstruction. To evaluate detection performance of human observer, D-DOG CHO, and the proposed network, we calculated percent correct (Pc) as a figure of merit. Our results show that the detectability of the proposed network containing 20 number of 11 by 11 convolution filters is most similar to that of human observer.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131611 (16 March 2020); doi: 10.1117/12.2549550
Show Author Affiliations
Changwoo Lee, Korea Research Institute of Standards and Science (Korea, Republic of)
Jongduk Baek, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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