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Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury [article]

Samuel Remedios, Snehashis Roy, Justin Blaber, Camilo Bermudez, Vishwesh Nath, Mayur B. Patel, John A. Butman, Bennett A. Landman, Dzung L. Pham
2019 arXiv   pre-print
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

Julie Wang, Alexander Wood, Chao Gao, Kayvan Najarian, Jonathan Gryak
2021 Entropy  
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

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
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]

Thomas Mistal, Pauline Roca, Christophe Maggia, Alan Tucholka, Florence Forbes, Senan Doyle, Alexandre Krainik, Damien Galanaud, Emmanuelle Schmitt, Stephane Kremer, Irene Tropres, Emmanuel Louis Barbier (+2 others)
2021 medRxiv   pre-print
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

Samuel Remedios, Snehashis Roy, Dzung L. Pham, John A. Butman, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
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

S.B. Strauss, N. Kim, C.A. Branch, M.E. Kahn, M. Kim, R.B. Lipton, J.M. Provataris, H.F. Scholl, M.E. Zimmerman, M.L. Lipton
2016 American Journal of Neuroradiology  
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

Joshua K. Wong, Günther Deuschl, Robin Wolke, Hagai Bergman, Muthuraman Muthuraman, Sergiu Groppa, Sameer A. Sheth, Helen M. Bronte-Stewart, Kevin B. Wilkins, Matthew N. Petrucci, Emilia Lambert, Yasmine Kehnemouyi (+39 others)
2022 Frontiers in Human Neuroscience  
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

Jasjit S Suri
2019 Frontiers in Bioscience  
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

Emily Smitherman, Ana Hernandez, Peter L. Stavinoha, Rong Huang, Steven G. Kernie, Ramon Diaz-Arrastia, Darryl K. Miles
2016 Journal of Neurotrauma  
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]

Haleh Akrami, Richard M Leahy, Andrei Irimia, Paul E Kim, Christianne Heck, Anand Joshi
2020 medRxiv   pre-print
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

Martin Cenek, Masa Hu, Gerald York, Spencer Dahl
2018 Frontiers in Robotics and AI  
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

Agata Sage, Pawel Badura
2020 Applied Sciences  
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

Regina E. Y. Kim, Minho Lee, Dong Woo Kang, Sheng-Min Wang, Nak-Young Kim, Min Kyoung Lee, Hyun Kook Lim, Donghyeon Kim
2020 Diagnostics  
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

Riccardo De Feo, Federico Giove
2019 Journal of Neuroscience Methods  
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

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
, 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 (  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu
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