Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

Umehara, Kensuke; Ota, Junko; Ishida, Takayuki

doi:10.4236/ojmi.2017.74018

Cited by 23 publications

(9 citation statements)

References 30 publications

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“…[5][6][7][8] In medical imaging, images are often acquired for specific purposes, and the use of objective measures of IQ is widely advocated for assessing imaging systems and image processing algorithms. [9][10][11][12][13][14][15] Although DL-SR algorithms can improve traditional IQ metrics, [16][17][18][19][20][21] it is well-known that such metrics may not always correlate with objective task-based IQ measures. [22][23][24][25] Despite this, relatively few studies have objectively assessed image superresolution methods.…”

Section: Introductionmentioning

confidence: 99%

Impact of deep learning-based image super-resolution on binary signal detection

et al. 2021

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. Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed using traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of IQ that are relevant to medical imaging tasks remains largely unexplored. We investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network and the super-resolution generative adversarial network, were trained using simulated medical image data. Binary signal-known-exactly with background-known-statistically and signal-known-statistically with background-known-statistically detection tasks were formulated. Numerical observers (NOs), which included a neural network-approximated ideal observer and common linear NOs, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task performance was quantified. In addition, the utility of DL-SR for improving the task performance of suboptimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR improved traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and even degraded it. It was observed that DL-SR improved the task performance of suboptimal observers under certain conditions. Conclusions: Our study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.

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Section: Introductionmentioning

confidence: 99%

Impact of deep learning-based image super-resolution on binary signal detection

et al. 2021

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“…The mapping is represented as a deep CNN that takes the low resolution image as the input and outputs the high resolution one. The study of Umehara et al [158] shows that SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography especially in dense breasts.…”

Section: Resultsmentioning

confidence: 99%

Deep convolutional neural networks for mammography: advances, challenges and applications

et al. 2019

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Background The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions. Results In this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths. Conclusions The mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community. Electronic supplementary material The online version of this article (10.1186/s12859-019-2823-4) contains supplementary material, which is available to authorized users.

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“…In medical imaging, it can improve image resolution without requiring changes in imaging hardware or scan protocols. The present literature on super‐resolution CNNs in medical imaging use postprocessing methodologies in organ systems and modalities that require high levels of fine detail such as chest radiography, 55 mammography, 56 and musculoskeletal MRI 57,58 . Translation of current deep‐learning MR reconstruction approaches to super‐resolution is a promising technique that merits further investigation.…”

Section: Future Perspectivesmentioning

confidence: 99%

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

Lin

Johnson

Knoll

et al. 2020

Magnetic Resonance Imaging

190

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Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep‐learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep‐learning‐based MR image reconstruction. We review the basic concepts of how deep‐learning algorithms aid in the transformation of raw k‐space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep‐learning‐based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep‐learning‐based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.

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Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

Cited by 23 publications

References 30 publications

Impact of deep learning-based image super-resolution on binary signal detection

Impact of deep learning-based image super-resolution on binary signal detection

Deep convolutional neural networks for mammography: advances, challenges and applications

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

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