Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique

Ge, Yongshuai; Liu, Peizhen; Ni, Yifan; Chen, Jianwei; Yang, Jiecheng; Su, Ting; Zhang, Huitao; Guo, Jr-Hung; Zheng, Hairong; Li, Zhicheng; Liang, Dong

doi:10.1109/tbme.2020.3011119

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…Through this adjustment, the intensities can be better distributed on the histogram. This allows for regions of lower local contrast to achieve a higher contrast [41]. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values [42,43].…”

Section: Histogram Equalizationmentioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.

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Section: Histogram Equalizationmentioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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“…Recently, with the rapid development of deep learning algorithms, deep learning has gradually become a new research hotspot in computational biology with its ability to detect hidden features in large-scale biological data to make predictions [ 14 ]. Given the good results achieved by the Convolutional Neural Network (CNN) [ 15 ] for tasks such as image classification, (like the applications to X-ray imaging [ 16 ]), CNN are receiving more attention from biologists. After numerous researches such as DeepSEA using CNN to identify functional effects of noncoding variants [ 17 ] and Basset which offers a powerful computational approach to annotate and interpret the noncoding genome by applying the CNN [ 18 ], the CNN has been the main method to capture the RBPs information in various deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites

et al. 2022

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Background Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. Results To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. Conclusion The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.

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“…To date, to the best of our knowledge, a single article has been published on the use of a data‐driven method for denoising of GI, and in particular differential phase contrast (DPC), projection data 18 . However, the authors used a black‐box model and restricted their analysis to radiography.…”

Section: Introductionmentioning

confidence: 99%

“…To date, to the best of our knowledge, a single article has been published on the use of a data-driven method for denoising of GI, and in particular differential phase contrast (DPC), projection data. 18 However, the authors used a black-box model and restricted their analysis to radiography. In contrast, we focus on GI phase CT and propose a hybrid denoising algorithm, which we call "Interpretable NonexpanSIve Data-Efficient network" (INSIDEnet), that attempts to leverage the strengths of both worlds:the interpretability of classical filters and the flexibility of data-driven models.…”

Section: Introductionmentioning

confidence: 99%

INSIDEnet: Interpretable NonexpanSIve Data‐Efficient network for denoising in grating interferometry breast CT

et al. 2022

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Purpose Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft‐tissue contrast. Grating interferometry breast computed tomography (GI‐BCT) is a promising X‐ray phase contrast modality that could overcome these limitations by offering high soft‐tissue contrast and excellent three‐dimensional resolution. To enable the transition of this technology to clinical practice, dedicated data‐processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. Methods This article proposes a novel denoising algorithm that can cope with the high‐noise amplitudes and heteroscedasticity which arise in GI‐BCT when operated in a low‐dose regime to effectively regularize the ill‐conditioned GI‐BCT inverse problem. We present a data‐driven algorithm called INSIDEnet, which combines different ideas such as multiscale image processing, transform‐domain filtering, transform learning, and explicit orthogonality to build an Interpretable NonexpanSIve Data‐Efficient network (INSIDEnet). Results We apply the method to simulated breast phantom datasets and to real data acquired on a GI‐BCT prototype and show that the proposed algorithm outperforms traditional state‐of‐the‐art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). Conclusions The proposed INSIDEnet is highly data‐efficient, interpretable, and outperforms state‐of‐the‐art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated plug‐and‐play GI‐BCT reconstruction framework, needed to translate this promising technology to the clinics.

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Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique

Cited by 14 publications

References 32 publications

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites

INSIDEnet: Interpretable NonexpanSIve Data‐Efficient network for denoising in grating interferometry breast CT

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