Implementation of an ideal observer model using convolutional neural network for breast CT images

Kim, Gihun; Han, Minah; Shim, Hyunjung; Baek, Jongduk

doi:10.1117/12.2512131

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In addition, it employs a nonlinear activation function and thus can provide higher detection performance than LG‐CHO and PLS‐CHO for SKE/BKS detection tasks in breast CT images. In a previous study, we implemented a CNN‐based model observer for circular signal only, and compared its performance with that of LG‐CHO. In this work, we first examined the performance of the CNN‐based model observer for SKE/BKE detection tasks with i.i.d.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 singlelayer 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, a good strategy to implement an anthropomorphic model observer is to use a convolutional neural network (CNN), which has shown good performance on image classification tasks (Krizhevsky et al 2012, Simonyan and Zisserman 2014, Li et al 2014, He et al 2016, Szegedy et al 2015. In most cases, CNNs have been implemented for the purpose of maximizing the classification accuracy and have thus been used as ideal model observers (Zhou et al 2019, Kim et al 2019 which produce maximum detectability.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks

Han

Baek

2020

Phys. Med. Biol.

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The purpose of this study is implementation of an anthropomorphic model observer using a convolutional neural network (CNN) for signal-known-statistically (SKS) and background-known-statistically (BKS) detection tasks. We conduct SKS/BKS detection tasks on simulated cone beam computed tomography (CBCT) images with eight types of signal and randomly varied breast anatomical backgrounds. To predict human observer performance, we use conventional anthropomorphic model observers (i.e. the non-prewhitening observer with an eye-filter, the dense difference-of-Gaussian channelized Hotelling observer (CHO), and the Gabor CHO) and implement CNN-based model observer. We propose an effective data labeling strategy for CNN training reflecting the inefficiency of human observer decision-making on detection and investigate various CNN architectures (from single-layer to four-layer). We compare the abilities of CNN-based and conventional model observers to predict human observer performance for different background noise structures. The three-layer CNN trained with labeled data generated by our proposed labeling strategy predicts human observer performance better than conventional model observers for different noise structures in CBCT images. This network also shows good correlation with human observer performance for general tasks when training and testing images have different noise structures.

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“…dose reduction) and computer aided diagnosis applications. In addition, some studies have been successful in applying neural networks for a MO detection task [15][16][17]. In this study, we use a convolutional network as a MO for a defect localization task with synthetic data, specifically the U-Net architecture.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based model observer by U-Net

Lorente

Abbey

Brankov

2020

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment

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Model Observers (MO) are algorithms designed to evaluate and optimize the parameters of new medical imaging reconstruction methodologies by providing a measure of human accuracy for a diagnostic task. In contrast with a computer-aided diagnosis system, MOs are not designed to outperform human diagnosis but only to find a defect if a radiologist would be able to detect it. These algorithms can economize and expedite the finding of optimal reconstruction parameters by reducing the number of sessions with expert radiologists, which are costly and prolonged. Convolutional Neural Networks (CNN or ConvNet) have been successfully used in the computer vision field for image classification, segmentation and video analytics. In this paper, we propose and test several U-Net configurations as MO for a defect localization task on synthetic images with different levels of correlated noisy backgrounds. Preliminary results show that the CNN based MO has potential and its accuracy correlates well with that of the human.

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Implementation of an ideal observer model using convolutional neural network for breast CT images

Cited by 3 publications

References 14 publications

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

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

A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks

Deep learning based model observer by U-Net

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