A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

Tavse, Sampada; Vijayakumar, V.; Bachute, Mrinal; Gite, Shilpa; Kotecha, Ketan

doi:10.3390/fi14120351

Cited by 10 publications

(5 citation statements)

References 159 publications

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“…Although they created a table with the number of metrics used, they did not provide definitions for these metrics in the study. Taves' work [23] is considered superior to ours due to its detailed definition and comparison of loss functions. This is because our study does not focus on specific machine learning techniques, making the comparison of loss functions less effective.…”

Section: Comparison With Previous Studies and Limitationsmentioning

confidence: 95%

“…However, these articles were not included for yield predictions to generate image predictions of growing plants. The study by Taves et al (2022) [23] is an SLR on Generative Adversarial Network (GAN) and brain images and a comprehensive survey of various GAN techniques. They searched the strings: "(Brain Imaging) OR (Brain Images)" AND "GAN OR Generative Adversarial Network" in two electrical databases.…”

Section: Related Workmentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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Section: Comparison With Previous Studies and Limitationsmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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“…This study established a systematic literature review (SLR) on the segmentation of isointense brain MRI using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). PRISMA is a well-known systematic review methodology that has been used in a variety of research domains, including the medical field ( 46 ), business ( 47 ) and safety mining ( 45 ). Because of its 27 evidence-based checklist and four-phase analysis, PRISMA is acceptable in the research area even if it is not a quality assessment approach.…”

Section: Methodsmentioning

confidence: 99%

Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review

Mhlanga,

Viriri

2023

Front. Med.

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IntroductionTo improve comprehension of initial brain growth in wellness along with sickness, it is essential to precisely segment child brain magnetic resonance imaging (MRI) into white matter (WM) and gray matter (GM), along with cerebrospinal fluid (CSF). Nonetheless, in the isointense phase (6-8 months of age), the inborn myelination and development activities, WM along with GM display alike stages of intensity in both T1-weighted and T2-weighted MRI, making tissue segmentation extremely difficult.MethodsThe comprehensive review of studies related to isointense brain MRI segmentation approaches is highlighted in this publication. The main aim and contribution of this study is to aid researchers by providing a thorough review to make their search for isointense brain MRI segmentation easier. The systematic literature review is performed from four points of reference: (1) review of studies concerning isointense brain MRI segmentation; (2) research contribution and future works and limitations; (3) frequently applied evaluation metrics and datasets; (4) findings of this studies.Results and discussionThe systemic review is performed on studies that were published in the period of 2012 to 2022. A total of 19 primary studies of isointense brain MRI segmentation were selected to report the research question stated in this review.

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“…The literature review provided by [12] helped me to comprehend the function of synthetic dummy images generated by GANs. They conducted a thorough search of the Web of Science and Scopus databases to locate pertinent studies over the previous six years.…”

Section: Generative Adversarial Network In Mri Generationmentioning

confidence: 99%

Untitled

2024

IJMIT

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Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices.The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.

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A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

Cited by 10 publications

References 159 publications

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review

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