Deep Learning in MR Image Processing

Lee, Doohee; Lee, Jingu; Ko, Jingyu; Yoon, Jaeyeon; Ryu, Kanghyun; Nam, Yoonho

doi:10.13104/imri.2019.23.2.81

Cited by 41 publications

(22 citation statements)

References 69 publications

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“…With the experimentation they had done, they found that the neural network is accurate as human beings in terms of identifying the bacteria on images. The utilization of support vector machine and random forest algorithms had given a good result when compared with the other neural network model for the identification of bacteria presence on images (Lee et al, 2019 ; Preetha & Pandi Selvi, 2018 ). They had also discussed and given various considerations to be considered by the other researchers when they were working on these neural network models to identify the species or the bacteria on 3D digital images (Cimpoi et al, 2016 ; Zieliński et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

Satyanarayana¹,

Rao

Bhattacharyya

et al. 2021

Multidim Syst Sign Process

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This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

Satyanarayana¹,

Rao

Bhattacharyya

et al. 2021

Multidim Syst Sign Process

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“…Recently, DL methods have been applied increasingly in the field of radiology, and they have demonstrated enormous potential in several MRI processing areas [17], including artifact correction for specific pulse sequences [18,19]. Thus, recent studies have developed DL algorithms using variants of CNN to remove synthetic FLAIR artifacts and have thus demonstrated the feasibility of this method.…”

Section: Discussionmentioning

confidence: 99%

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

Ryu

Baek

Gho³

et al. 2020

JCM

Self Cite

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We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI).

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“…Therefore, CNNs with some special architectures are gradually popular to be employed to perform classification tasks in bioinformatics 38 , 39 . Inspired by the excellent performance of deep learning in image processing 40 , 41 , natural language processing 42 , 43 and many other fields 44 , 45 , a deep learning framework is constructed in this study by a CNN with an embedding layer to automatically learn a suitable representation of the raw data, discover high-level features, and improve ORI identification performance over traditional models.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework combined with word embedding to identify DNA replication origins

Zhang

2021

Sci Rep

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The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote’s ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, ‘Word2vec’, to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew’s correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.

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Deep Learning in MR Image Processing

Cited by 41 publications

References 69 publications

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

A deep learning framework combined with word embedding to identify DNA replication origins

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