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Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection
<span title="2022-03-04">2022</span>
<i title="Hindawi Limited">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r5ecdsgx3bafnpd5wn625laa2i" style="color: black;">Contrast Media & Molecular Imaging</a>
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The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma ...
In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. ...
Before surgery, the patients were scanned using magnetic resonance technology to obtain imaging data, determine the regional scope, boundary, and nerve conduction bundle in the adjacent region of glioma ...
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Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
<span title="2018-11-15">2018</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a>
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But previous studies on magnetic resonance imaging (MRI) images were not effective enough. ...
AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. ...
Magnetic resonance imaging (MRI) has become the essential way for glioma diagnosis before surgery. ...
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Machine Learning Theory and Applications for Healthcare
<span title="">2017</span>
<i title="Hindawi Limited">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cswd2rqrire6lgrsm56kv4adue" style="color: black;">Journal of Healthcare Engineering</a>
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An automatic method for segmentation of 3D magnetic resonance imaging (MRI) data, useful in the clinical diagnosis of brain tumor, named as Glioma is presented by Z. ...
Lama et al. proposes a method and compared Alzheimer disease (AD) diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment, and healthy control ...
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REVIEW ON DEEP LEARNING APPROACH FOR BRAIN TUMOR GLIOMA ANALYSIS
<span title="2021-03-01">2021</span>
<i title="Auricle Technologies, Pvt., Ltd.">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ti2mgqbqpfhhzla5hdk62fee2y" style="color: black;">Information Technology in Industry</a>
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This paper outlines a review on the developments of MRI sample processing for early diagnosis for brain tumor glioma diagnosis using deep learning approach. ...
Deep learning approach has shown a benefit of image coding based on selective features and state of art processing in diagnosis. ...
and Magnetic Resonance Imaging (MRI) are used in the diagnosis of early prediction of cancer in patients. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17762/itii.v9i1.144">doi:10.17762/itii.v9i1.144</a>
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Reverse Engineering Glioma Radiomics to Conventional Neuroimaging
<span title="2021-08-06">2021</span>
<i title="Japan Neurosurgical Society">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pqpzwdnlqbbftltmwmw3lbnyw4" style="color: black;">Neurologia medico-chirurgica</a>
</i>
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This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic ...
alteration of gliomas noninvasively. ...
J Magn Reson Imaging 54: 197-205, 2021 16) Bhandari AP, Liong R, Koppen J, Murthy SV,
Matsui Y, Maruyama T, Nitta M, et al.: Prediction of lower-grade glioma molecular subtypes using deep learning. ...
<span class="external-identifiers">
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<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34373429">pmid:34373429</a>
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Clinical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in the Imaging of Gliomas: A Systematic Review
<span title="2021-11-14">2021</span>
<i title="Cureus, Inc.">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/74mctp3frjfttdv6dx4dzh6lbm" style="color: black;">Cureus</a>
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In neuro-oncology, magnetic resonance imaging (MRI) is a critically important, non-invasive radiologic assessment technique for brain tumor diagnosis, especially glioma. ...
Deep learning improves MRI image characterization and interpretation through the utilization of raw imaging data and provides unprecedented enhancement of images and representation for detection and classification ...
Both dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) have been used prior to surgery to differentiate the grades ...
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Noninvasive Glioma Grading with Deep Learning: A Pilot Study
[chapter]
<span title="2022-06-06">2022</span>
<i title="IOS Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4ju2ftoebbcvrjj6lqnnbh42wq" style="color: black;">Studies in Health Technology and Informatics</a>
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Our pilot study aimed to evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I–IV) based on preoperative magnetic resonance imaging (MRI) from Burdenko Neurosurgery ...
Our preliminary results proved the separability of MR T1 axial images with contrast enhancement by WHO grade using DL. ...
Data preprocessing was supported by Russian Foundation for Basic Research (grant 18-29-01052).
G. Danilov et al. / Noninvasive Glioma Grading with Deep Learning: A Pilot Study ...
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Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging
<span title="2019-06-14">2019</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2zwku6u6nfdcri773tpisi6ldi" style="color: black;">Cancers</a>
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Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. ...
This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
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A novel deep learning algorithm for the automatic detection of high-grade gliomas on t2-weighted magnetic resonance images. a preliminary machine learning study
<span title="">2019</span>
<i title="Turkish Neurosurgical Society">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t74vnn62jrfetpys2anqx4isnq" style="color: black;">Turkish Neurosurgery</a>
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To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. ...
A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. ...
Magnetic resonance imaging (MRI) is the first-line imaging modality in the diagnosis and follow-up of HGGs (15) . ...
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<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31608975">pmid:31608975</a>
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Brain Tumor Classification Using Deep Neural Network
<span title="">2020</span>
<i title="ASTES Journal">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5z5fhzfgard2vod5er2hdyuq54" style="color: black;">Advances in Science, Technology and Engineering Systems</a>
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Computer aided diagnostic systems and deep neural network architectures can be used in the diagnosis of multicentric gliomas and multiple lesions. ...
In this study, the Deep Neural Network classification model with Synthetic Minority Over-sampling Technique pre-processing was used on the Visually Accessible Rembrandt Images dataset. ...
With the development of computed tomography and magnetic resonance imaging (MRI), it has become increasingly clear that gliomas may have a multifocal or multicentric [6] . ...
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BIMG-22. DEEP LEARNING SUPER-RESOLUTION MR SPECTROSCOPIC IMAGING TO MAP TUMOR METABOLISM IN MUTANT IDH GLIOMA PATIENTS
<span title="2020-03-01">2020</span>
<i title="Oxford University Press (OUP)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oyrc2vzjqfdajldnwvimd3mcmq" style="color: black;">Neuro-Oncology Advances</a>
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We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. ...
Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. ...
We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. ...
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Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma
<span title="2021-10-04">2021</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mefn5t5kjndavkrqau5bzfpaoe" style="color: black;">Frontiers in Oncology</a>
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This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with ...
glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). ...
J-MZ, XL, and JZ collected the molecular pathology and image data and performed pre-processing. HC and FL analyzed the data and performed the statistical analysis. HC wrote the manuscript. ...
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A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor
<span title="">2020</span>
<i title="ScopeMed Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y732wtgiujc5jmozu6q2pzzk64" style="color: black;">Acta Informatica Medica</a>
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In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. ...
The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. ...
The first dataset consisted of 900 MRI images of 159 patients that suffered from low-grade glioma and the second dataset consisted of 1061 MRI images of 199 patients that suffered from low-grade glioma ...
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IMG-19. RADIOMICS AND SUPERVISED DEEP LEARNING TO PREDICT MOLECULAR SUBGROUPS IN MEDULLOBLASTOMA BASED ON WHOLE TUMOR VOLUME LABELING: A SINGLE CENTER MULTIPARAMETRIC MR ANALYSIS
<span title="2020-12-01">2020</span>
<i title="Oxford University Press (OUP)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2dt5xbyp3zbx3m4m43pdhhrnme" style="color: black;">Neuro-Oncology</a>
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MATERIALS AND METHODS We retrospectively evaluated 42 patients with histological diagnosis of MB, known molecular subgroup, and diagnostic MRI scan performed in our Institution on a 3 Tesla magnet. ...
Remaining features were used to predict MB subgroup with a Random Forest algorithm(R). The most relevant features were ranked based on Gini index (R). ...
if a diagnosis of IDH mutant glioma could be made confidently using imaging data. ...
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Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis
<span title="2021-05-31">2021</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mefn5t5kjndavkrqau5bzfpaoe" style="color: black;">Frontiers in Oncology</a>
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Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images ( ...
This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression ...
In this study, we used a convolutional neural network (Co nvN et) to build a r adi omi cs model based on multiparametric magnetic resonance imaging (MRI) to predict SYP expression levels in patients with ...
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