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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Meera Srikrishna, Joana B. Pereira, Rolf A. Heckemann, Giovanni Volpe, Danielle van Westen, Anna Zettergren, Silke Kern, Lars-Olof Wahlund, Eric Westman, Ingmar Skoog, Michael Schöll
2021 NeuroImage  
A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels.  ...  Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.  ...  In future work, we plan to compare manual CT annotations to deep learning-derived CT-based model predictions trained from MR-derived labels.  ... 
doi:10.1016/j.neuroimage.2021.118606 pmid:34571160 fatcat:7uh3pqtfcjak3b6b4mhb7jyxrm

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Meera Srikrishna, Rolf A. Heckemann, Joana B. Pereira, Giovanni Volpe, Anna Zettergren, Silke Kern, Eric Westman, Ingmar Skoog, Michael Schöll
2022 Frontiers in Computational Neuroscience  
For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both  ...  We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT.  ...  Previously, we conducted a study exploring the possibility of using MR-derived brain tissue class labels to train deep learning models to perform brain tissue classification in head CTs (Srikrishna et  ... 
doi:10.3389/fncom.2021.785244 pmid:35082608 pmcid:PMC8784554 fatcat:jis5ievmhzapddlrorhsa7mva4

Prospects of deep learning for medical imaging

Jonghoon Kim, Jisu Hong, Hyunjin Park
2018 Precision and Future Medicine  
Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly.  ...  First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given.  ...  In summary, there is no one network structure that solves all the medical imaging Prediction MRI Brain Risk prediction [81] Classification CT Lung Lesion classification [82] US, CT Breast Lesion classification  ... 
doi:10.23838/pfm.2018.00030 fatcat:2bclzigfijadzcdoqhhzniqwdy

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
In many applications, machine learning based systems have shown comparable performance to human decision-making.  ...  By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.  ...  Acknowledgment Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Rima Hajjo, Dima A Sabbah, Sanaa K Bardaweel, Alexander Tropsha
2021 Diagnostics  
We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical  ...  The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity.  ...  Deep learning methods primarily differ from "classical" machine learning approaches by focusing on feature learning, i.e., automatically learning representations of data [103] .  ... 
doi:10.3390/diagnostics11050742 pmid:33919342 pmcid:PMC8143297 fatcat:d5k6655lbfamzhjleyso5q54u4

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Wanying Gao, Chunyan Wang, Qiwei Li, Xijing Zhang, Jianmin Yuan, Dianfu Li, Yu Sun, Zaozao Chen, Zhongze Gu
2022 Frontiers in Bioengineering and Biotechnology  
, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role.  ...  Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the  ...  Since MRI is good at depicting human soft tissues, especially the human brain, segmentation of MRI has attracted great interest from researchers.  ... 
doi:10.3389/fbioe.2022.985692 pmid:36172022 pmcid:PMC9511994 fatcat:bfu4v4k6tndxxexazxvrgvcc6m

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise  ...  Few-shot learning (FSL) aims to automatically and efficiently solve new tasks with few labeled samples based on knowledge obtained from previous experiences.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Deep Neural Network-Based Brain Tumor Detection Utilizing CT-Scan Images

Bisma Mushtaq, Dr. Satish Saini
2022 International Journal for Research in Applied Science and Engineering Technology  
The company has focused more on computer-assisted diagnostic research that uses images of tumors from medical data.  ...  It provides the segmentation and classification of tumor images as well as the diagnosis approaches based on CNN to help physicians recognize cancers.  ...  Clinical symptoms that form from epithelial tissue or hypodermis, alternately, are known by the labels cancer and sarcoma.  ... 
doi:10.22214/ijraset.2022.46135 fatcat:da4gadyofbhsxht562ohx3yqi4

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Khurram Ejaz, Mohd Shafry Mohd Rahim, Muhammad Arif, Diana Izdrui, Daniela Maria Craciun, Oana Geman, M. Pallikonda Rajasekaran
2022 Contrast Media & Molecular Imaging  
The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image.  ...  All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities.  ...  Accordingly, from the MRI supervoxel images, labels were transferred to the atlas.  ... 
doi:10.1155/2022/1541980 pmid:35919500 pmcid:PMC9293518 fatcat:5oed3yl2nnbghieoast7qf6hrq

Advances in Deep Learning-Based Medical Image Analysis

Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu
2021 Health Data Science  
Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets  ...  With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active  ...  Acknowledgments This study was supported in part by grants from the Zhejiang Provincial Key Research & Development Program (No. 2020C03073).  ... 
doi:10.34133/2021/8786793 fatcat:d6nkb4yoxrcgni4y5owju5pnh4

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
These days, DL systems are cutting-edge ML systems spanning a broad range of disciplines, from human language processing to video analysis, and commonly used in the scholarly world and enterprise sector  ...  This model was a kind of convolutional neural system (CNN) called deep learning (DL). Since then, researchers have started to participate efficiently in DL's fastest developing area of research.  ...  Zhao et al. suggested a system for data synthesis to produce segmentations masks and pairs of brain MRI scans from just one labelled MRI scan.  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m

Deep into the Brain: Artificial Intelligence in Stroke Imaging

Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang
2017 Journal of Stroke  
Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results.  ...  Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine.  ...  Deep learning Deep learning is a more recently developed technique of machine learning, which mimics the human brain using multiple layers of ANN.  ... 
doi:10.5853/jos.2017.02054 pmid:29037014 pmcid:PMC5647643 fatcat:sbvi7boytndjfkk7em57aopqse

Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

Hala Shaari, Jasmin Kevrić, Samed Jukić, Larisa Bešić, Dejan Jokić, Nuredin Ahmed, Vladimir Rajs
2021 Brain Sciences  
resonance imaging MRI / computed tomography CT) findings.  ...  This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning.  ...  Table 1 . 1 Pediatric brain tumor detection and classification studies based on deep learning.  ... 
doi:10.3390/brainsci11060716 pmid:34071202 fatcat:usmduuhzyzcsrh7lgto3ejzbfu

A Review on Computer Aided Diagnosis of Acute Brain Stroke

Mahesh Anil Inamdar, Udupi Raghavendra, Anjan Gudigar, Yashas Chakole, Ajay Hegde, Girish R. Menon, Prabal Barua, Elizabeth Emma Palmer, Kang Hao Cheong, Wai Yee Chan, Edward J. Ciaccio, U. Rajendra Acharya
2021 Sensors  
(ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation.  ...  The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke  ...  Carlos CMD et al., presented an IoT enabled framework with CNN as the main classifier to identify a healthy or a stroke affected brain from CT images [172] .  ... 
doi:10.3390/s21248507 pmid:34960599 pmcid:PMC8707263 fatcat:zc4gtjhkoje2jotcqr5gvlatu4

MRI Image Based Relatable Pixel Extraction with Image Segmentation for Brain Tumor Cell Detection Using Deep Learning Model

Rajeshwari Dharavath, Kattula Shyamala
2021 Asian journal of convergence in technology  
The use of Deep Neural Networks classification for automatic brain tumor detection is proposed.  ...  The proposed a Relatable Pixel Extraction with Magnetic Resonance Imaging (MRI) Image Segmentation for Brain Tumor Cell Detection (RPEIS-BTCD) using Deep Learning Model.  ...  For categorizing brain tumour types from MRI image segments, a deep learning architecture based on 2D convolutional neural networks was used.  ... 
doi:10.33130/ajct.2021v07i03.005 fatcat:tovw7ejmjvaipltsuexexsbwpe
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