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Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
[article]
2019
arXiv
pre-print
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Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. ...
Three neural networks are trained to convergence on a computed tomography (CT) brain hematoma segmentation task: one only with NIH data,one only with VUMC data, and one multi-site model alternating between ...
schema on the segmentation of hematoma in traumatic brain injury (TBI) CT scans. ...
arXiv:1903.04207v1
fatcat:6zai3pb4efhptccucyq65x5anm
Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning
2021
Entropy
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While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is ...
CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. ...
In this study, the widely used box counting method [17] was employed to estimate D f for each binary image of segmentation, after which the fractal dimension D f was calculated for each frame in the ...
doi:10.3390/e23040382
pmid:33804831
fatcat:lol7wa6m4jhplp5lccdsiz72zm
Front Matter: Volume 10575
2018
Medical Imaging 2018: Computer-Aided Diagnosis
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The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
10575 1D
Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional
neural networks [10575-48]
SESSION 11
LUNG II
10575 1E
Comparing deep learning models for population ...
doi:10.1117/12.2315758
fatcat:kqpt2ugrxrgx7m5rhasawarque
Automatic quantification of brain lesion volume from post-trauma MR Images
[article]
2021
medRxiv
pre-print
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brain images according to the intensity, form and location of lesion observed in real TBI cases; ii) severe TBI patients (n=12 patients) who underwent MR imaging within 10 days after injury. ...
The determination of the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming which inhibits the practice in clinical routine. ...
(2015) Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 21:40-58 All rights reserved. No reuse allowed without permission. ...
doi:10.1101/2021.04.24.21255599
fatcat:7avws22bjbbrbm3vjv3yoi4z2m
Classifying magnetic resonance image modalities with convolutional neural networks
2018
Medical Imaging 2018: Computer-Aided Diagnosis
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2018 pre-print arXiv:1804.05764v1 fatcat:eoodb5iqp5crfi7g4iqpltpk64Other Versions
We present a novel 3D deep convolutional neural network (CNN)-based method for MR image contrast classification. ...
A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. ...
ACKNOWLEDGEMENTS Support for this work included funding from the Intramural Research Program of the NIH and the Department of Defense in the Center for Neuroscience and Regenerative Medicine. ...
doi:10.1117/12.2293943
dblp:conf/micad/RemediosPBR18
fatcat:2goze4zhkfhxtpko4zqkn2be5q
Bidirectional Changes in Anisotropy Are Associated with Outcomes in Mild Traumatic Brain Injury
2016
American Journal of Neuroradiology
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MATERIALS AND METHODS: DTI was performed on 39 subjects with mild traumatic brain injury within 16 days of injury and 40 controls; 26 subjects with mild traumatic brain injury returned for follow-up at ...
This prospective longitudinal study identifies early diffusion tensor imaging biomarkers of mild traumatic brain injury that significantly relate to outcomes at 1 year following injury. ...
axonal injury. 2, 3 The inability of imaging techniques such as CT and MR imaging to detect traumatic axonal injury has led to delayed understanding of the clinical mTBI syndrome, despite human (eg, ...
doi:10.3174/ajnr.a4851
pmid:27282864
pmcid:PMC5148740
fatcat:4yjhqsz4x5dgrjluuf3rtcnrr4
Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury
2022
Frontiers in Human Neuroscience
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pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury. ...
After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. ...
AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication. ...
doi:10.3389/fnhum.2022.813387
pmid:35308605
pmcid:PMC8931265
fatcat:tgoabnprireifa2o5n5jwvrkja
State-of-the-art review on deep learning in medical imaging
2019
Frontiers in Bioscience
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Review on deep learning in medical imaging 393 ...
We would like to thank the publishers for approving usage of images in our paper. We would like to thank MediaLab Asia, DEITY for their encouragement and support. ...
Brain lesion segmentation In brain imaging, exact estimation of location of lesion from traumatic brain injury (TBI) pertinent to brain structure is a necessity. ...
doi:10.2741/4725
fatcat:lh5b3okh4jcq5aogjfowjfdaqy
Predicting Outcome after Pediatric Traumatic Brain Injury by Early Magnetic Resonance Imaging Lesion Location and Volume
2016
Journal of Neurotrauma
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Brain lesions after traumatic brain injury (TBI) are heterogeneous, rendering outcome prognostication difficult. ...
The aim of this study is to investigate whether early magnetic resonance imaging (MRI) of lesion location and lesion volume within discrete brain anatomical zones can accurately predict long-term neurological ...
Michael Morriss for his assistance in the interpretation of neuroradiology results and Evin Shirley for her research coordinator support. ...
doi:10.1089/neu.2014.3801
pmid:25808802
pmcid:PMC4700399
fatcat:hjmkfi7flvgzviwnlj4v77lkda
Neuroanatomic markers of post-traumatic epilepsy based on magnetic resonance imaging and machine learning
[article]
2020
medRxiv
pre-print
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Although post-traumatic epilepsy (PTE) is a common complication of traumatic brain injury (TBI), the relationship between these conditions is unclear, early PTE detection and prevention being major unmet ...
We performed tensor-based morphometry to analyze brain shape changes associated with TBI and to derive imaging features for statistical group comparison. ...
TRACK-TBI Pilot dataset This is a multi-site study with data across the injury spectrum, along with CT/MRI imaging, blood biospecimens, and detailed clinical outcomes [52] . ...
doi:10.1101/2020.07.22.20160218
fatcat:jnmy6whrtbfpzkbfrpl3yvmj4m
Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities
2018
Frontiers in Robotics and AI
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This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and ...
The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical ...
We will discuss them loosely in both contexts of tumor segmentation and traumatic brain injury. ...
doi:10.3389/frobt.2018.00120
pmid:33500999
pmcid:PMC7805910
fatcat:jlf2hau7xjdqflhlqunygyxg6i
Intracranial Hemorrhage Detection in Head CT Using Double-Branch Convolutional Neural Network, Support Vector Machine, and Random Forest
2020
Applied Sciences
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The experiments involved 372,556 images from 11,454 CT series of 9997 patients, with each image annotated with labels related to the hemorrhage subtypes. ...
We validated deep networks from both branches of our framework and the model with either of two classifiers under consideration. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/app10217577
fatcat:j4shrqmo3vewxghaidbizms46q
Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI
2020
Diagnostics
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This study aimed to establish East Asian normative brain data using multi-site MRI and determine the robustness of these data for clinical research. ...
Normative brain magnetic resonance imaging (MRI) is essential to interpret the state of an individual's brain health. However, a normative study is often expensive for small research groups. ...
Deep Learning Segmentation Our in-house segmentation tool was developed from the existing UNet++ deep learning architecture with a three-dimensional methodology to train 104 labels. ...
doi:10.3390/diagnostics11010013
pmid:33374745
fatcat:aeolylmwjffcnia5a3pv5tc6ci
Towards an efficient segmentation of small rodents brain: a short critical review
2019
Journal of Neuroscience Methods
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Furthermore, we will briefly address the emerging Deep Learning methods for the segmentation of medical imaging, and the perspectives for applications to small rodents. ...
While many segmentation schemes have been developed for the human brain, fewer are available for rodent MRI, often by adaptation from human neuroimaging. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies. ...
doi:10.1016/j.jneumeth.2019.05.003
pmid:31102669
fatcat:bv6m3zdlebfhbpf4htn4yhad2y
Applications of Deep Learning and Reinforcement Learning to Biological Data
2018
IEEE Transactions on Neural Networks and Learning Systems
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, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. ...
Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. ...
Kamal Abu-Hassan for useful discussions during the early stage of the work. This work was supported by the ACSLab (www.acslab.info). ...
doi:10.1109/tnnls.2018.2790388
pmid:29771663
fatcat:6r63zihrfvea7cto4ei3mlvqtu
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