In this work, we proposed a non-linear observer model based on convolutional neural network and compare its performance with LG-CHO for four alternative forced choice detection task using simulated breast CT images. In our network, each convolutional layer contained 3×3 filters and a leaky-ReLU as an activation function, but a pooling layer and a zero padding to the output of each convolutional layer were not used unlike general convolutional neural network. Network training was conducted using ADAM optimizer with two design parameters (i.e., network depth and width). The optimal value of the design parameter was found by brute force searching, which spanned up to 30 for depth and 128 for channel, respectively. To generate training and validation dataset, we generated anatomical noise images using a power law spectrum of breast anatomy. 50% volume glandular fraction was assumed, and 1 mm diameter signal was used for detection task. The generated images were recon- structed using filtered back-projection with a fan beam CT geometry, and ramp and Hanning filters were used as an apodization filter to generate different noise structures. To train our network, 125,000 signal present images and 375,000 signal absent images were reconstructed for each apodization filter. To measure detectability, we used percent correction with 4,000 images, generated independently from training and validation dataset. Our results show that the proposed network composed of 30 layers and 64 channels provides higher detectability than LG-CHO. We believe that the improved detectability is achieved by the presence of the non-linear module (i.e., leaky-ReLU) in the network.
|Title of host publication||Medical Imaging 2019|
|Subtitle of host publication||Image Perception, Observer Performance, and Technology Assessment|
|Editors||Robert M. Nishikawa, Frank W. Samuelson|
|Publication status||Published - 2019|
|Event||Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States|
Duration: 2019 Feb 20 → 2019 Feb 21
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment|
|Period||19/2/20 → 19/2/21|
Bibliographical noteFunding Information:
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the ICT Consilience Creative Program (IITP-2018-2017-0-01015) supervised by the Institute for Information & communications Technology Promotion (IITP), and by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A1A1A05077894, 2018M3A9H6081483, 2017M2A2A4A01070302, 2017M2A2A6A01019663, and 2017M2A2A6A02087175).
© 2019 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging