Purpose: In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images. Methods: We first showed that the CNN-based model observer provided similar detection performance to the ideal observer (IO) for signal-known-exactly and background-known-exactly detection tasks with an uncorrelated Gaussian background noise image. We then demonstrated that a single-layer CNN without a nonlinear activation function provided similar detection performance in breast CT images to the Hotelling observer (HO). To train the CNN-based model observer, we generated simulated breast CT images to produce a training dataset in which different background noise structures were generated using filtered back projection with a ramp, or a Hanning weighted ramp, filter. Circular, elliptical, and spiculated signals were used for the detection tasks. The optimal depth and the number of channels for the CNN-based model observer were determined for each task. The detection performances of the HO and a channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) and partial least squares (PLS) channels were also estimated for comparison. Results: The results showed that the CNN-based model observer provided higher detection performance than the HO, LG-CHO, and PLS-CHO for all tasks. In addition, it was shown that the proposed CNN-based model observer provided higher detection performance than the HO using a smaller training dataset. Conclusions: In the presence of nonlinearity in the CNN, the proposed CNN-based model observer showed better performance than other linear observers.
|Number of pages||14|
|Publication status||Published - 2020 Apr 1|
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (NRF) (NRF‐2019R1A2C2084936 and 2018M3A9H6081483).
This research was supported by the National Research Foundation of Korea (NRF) (NRF-2019R1A2C2084936 and 2018M3A9H6081483).
© 2020 American Association of Physicists in Medicine
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging