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.
|Title of host publication||Medical Imaging 2020|
|Subtitle of host publication||Image Perception, Observer Performance, and Technology Assessment|
|Editors||Frank W. Samuelson, Sian Taylor-Phillips|
|Publication status||Published - 2020|
|Event||Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment - Houston, United States|
Duration: 2020 Feb 19 → 2020 Feb 20
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment|
|Period||20/2/19 → 20/2/20|
Bibliographical noteFunding Information:
Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Min- istry of Science and ICT (2018M3A9H6081482, 2018M3A9H6081483, 2018R1A1A1A05077894, 2017M2A2A4A01 070302, 2017M2A2A6A01019663, 2017M2A2A6A02087175).
Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (2018M3A9H6081482, 2018M3A9H6081483, 2018R1A1A1A05077894, 2017M2A2A4A01 070302, 2017M2A2A6A01019663, 2017M2A2A6A02087175).
© 2020 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging