Image reconstruction using priors from deep learning

Ayyagari, Devi; Ramesh, Nisha; Yatsenko, Dimitri; Taşdizen, Tolga; Atria, Cristain

doi:10.1117/12.2293766

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Recently, deep learning methods have demonstrated strong capability in many computer vision areas, and researchers have begun to apply deep learning on CT reconstruction . For instance, regularizations trained by neural networks have been investigated for SIR of low‐dose CT to capture complex image features . It can be expected that more deep learning‐based regularization strategies will be investigated in the near future.…”

Section: Discussionmentioning

confidence: 99%

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Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

et al. 2018

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Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

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Section: Discussionmentioning

confidence: 99%

“…175 For instance, regularizations trained by neural networks have been investigated for SIR of low-dose CT to capture complex image features. [176][177][178][179][180][181] It can be expected that more deep learning-based regularization strategies will be investigated in the near future.…”

Section: A Construction Of the Objective Functionmentioning

confidence: 99%

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

et al. 2018

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AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

et al. 2020

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Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high-and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.

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Fifty years of SPIE Medical Imaging proceedings papers

et al. 2022

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. Purpose: To commemorate the 50th anniversary of the first SPIE Medical Imaging meeting, we highlight some of the important publications published in the conference proceedings. Approach: We determined the top cited and downloaded papers. We also asked members of the editorial board of the Journal of Medical Imaging to select their favorite papers. Results: There was very little overlap between the three methods of highlighting papers. The downloads were mostly recent papers, whereas the favorite papers were mostly older papers. Conclusions: The three different methods combined provide an overview of the highlights of the papers published in the SPIE Medical Imaging conference proceedings over the last 50 years.

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Image reconstruction using priors from deep learning

Cited by 4 publications

References 5 publications

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

Fifty years of SPIE Medical Imaging proceedings papers

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