Image Quality Enhancement of Digital Breast Tomosynthesis Images by Deblurring with Deep Residual Convolutional Neural Network

Choi, Yunsu; Shim, Hyunjung; Baek, Jongduk

doi:10.1109/nssmic.2018.8824402

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The effectiveness of the proposed deblurring method is analyzed by comparing the restored frequency components between the CBCT and deblurred images. Experiments with 3D breast volume datasets demonstrate that our proposed network achieves excellent deblurring compared to the network described in our previous study [15].…”

Section: Plos Onementioning

confidence: 67%

“…We compared the proposed two-phase learning-based scheme with the FDK algorithm, totalvariation iterative reconstruction with GP-BB (TV-IR), and DRCNN [15]. In TV-IR method, we applied the algorithm by setting the iteration number to 100 and regularization parameter (i.e., λ) to 5 × 10 −4 .…”

Section: Resultsmentioning

confidence: 99%

“…However, due to the robust characteristics of CNN and its wide receptive field, a more accurate deblurring kernel with spatially varying properties can be estimated. In our previous work [15], we proposed a method to deblur DBT images using a deep residual-block-based CNN (DRCNN), where the cone-beam computed tomography (CBCT) images reconstructed by the FDK algorithm were used as target images. As the CBCT data were acquired over a 360˚range, the reconstructed image did not contain blurring artifacts caused by insufficient view sampling.…”

Section: Introductionmentioning

confidence: 99%

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Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images

et al. 2022

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In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited number of angles. Consequently, the reconstructed images contain severe blurring artifacts that might heavily degrade the DBT image quality and cause difficulties in detecting lesions. In this study, we propose a two-phase learning approach for artifact compensation in a coarse-to-fine manner to mitigate blurring artifacts effectively along all viewing directions of the DBT image volume (i.e., along the axial, coronal, and sagittal planes) to improve the detection performance of lesions. The proposed method employs a convolutional neural network model comprising two submodels/phases, with Phase 1 performing three-dimensional (3D) deblurring and Phase 2 performing additional 2D deblurring. To investigate the effects of loss functions on the proposed model’s deblurring performance, we evaluated several loss functions, such as the pixel-based loss function, adversarial-based loss function, and perception-based loss function. Compared with the DBT image, the mean squared error of the image and the root mean squared errors of the gradient of the image decreased by 82.8% and 44.9%, respectively, and the contrast-to-noise ratio increased by 183.4% in the in-focus plane. We verified that the proposed method sequentially restored the missing frequency components as the DBT images were processed through the Phase 1 and Phase 2 steps. These results indicate that the proposed method performs effective 3D deblurring, significantly reducing the blurring artifacts in the in-focus plane and other planes of the DBT image, thus improving the detection performance of lesions.

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Section: Plos Onementioning

confidence: 67%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images

et al. 2022

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“…The 3D model is constructed using digital breast tomosynthesis, which reconstructs numerous projections of low-dose pictures produced by a digital X-ray source moving over a limited arc angle [5]. When compared to mammography, the DBT allows the lesion to be segregated from the breast tissue background, resulting in improved detectability, thus overcoming the tissue overlapping challenge that faces traditional mammography [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

2022

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Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learning architecture (Mod_AlexNet). To the proposed modified deep learning architecture (Mod AlexNet), an optimization layer with multiple high performing optimizers is incorporated so that it can be evaluated and optimised using various optimization techniques. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark dataset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenario, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively.

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CNN-SVM with Data Augmentation for Robust Blur Detection of Digital Breast Tomosynthesis Images

Harron,

Sulaiman,

Osman

et al. 2023

Intelligent Multimedia Signal Processing for Smart Ecosystems

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Image Quality Enhancement of Digital Breast Tomosynthesis Images by Deblurring with Deep Residual Convolutional Neural Network

Cited by 4 publications

References 6 publications

Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images

Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images

Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

CNN-SVM with Data Augmentation for Robust Blur Detection of Digital Breast Tomosynthesis Images

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