A convolutional neural network-based model observer for breast CT images

Gihun Kim, Minah Han, Hyunjung Shim, Jongduk Baek

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)1619-1632
Number of pages14
JournalMedical physics
Volume47
Issue number4
DOIs
Publication statusPublished - 2020 Apr 1

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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