방사선의학 웹진

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Abstract
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.

Author information

Kim G1, Han M1, Shim H1, Baek J1.
1
School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, 162-1, Incheon, South Korea.