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Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers, Georgia D. Tourassi, Samuel G. Armato
2016 Medical Imaging 2016: Computer-Aided Diagnosis  
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.  ...  Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.  ...  It demonstrates that deep convolutional networks (ConvNets) can be useful for detection tasks, such as the detection of fractures in spine CT.  ... 
doi:10.1117/12.2217146 dblp:conf/micad/RothWYLBS16 fatcat:zqhf6rnp7bbjblsfi5t7chcriy

Front Matter: Volume 9785

2016 Medical Imaging 2016: Computer-Aided Diagnosis  
The CID Number appears on each page of the manuscript.  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  DEEP LEARNING I 9785 0P Deep convolutional networks for automated detection of posterior-element fractures on spine CT [9785-24] 9785 0Q Increasing CAD system efficacy for lung texture analysis  ... 
doi:10.1117/12.2240961 dblp:conf/micad/X16 fatcat:b5addnksdrgp3ixwvbjt53xeqe

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs

Hsuan-Yu Chen, Benny Wei-Yun Hsu, Yu-Kai Yin, Feng-Huei Lin, Tsung-Han Yang, Rong-Sen Yang, Chih-Kuo Lee, Vincent S. Tseng, Yan Chai Hum
2021 PLoS ONE  
This study uses a deep convolutional neural network (DCNN) to identify the feasibility for the screening, detection, and localization of VFs using PARs.  ...  Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures.  ...  Convolutional neural networks (CNN) [3] are capable of processing data in the form of images, videos, signals, sequences, and so on.  ... 
doi:10.1371/journal.pone.0245992 pmid:33507982 fatcat:jrzlcdzd2ze57gfxkegbfz2uui

Editorial Message from the Editor-in-Chief

João Manuel R. S. Tavares
2019 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
et al.; and 11) the development and implementation of a highly automated CT-based method to quantify 3D posterior element anatomy was addressed by Singh et al..  ...  compared four different loss functions for deep convolutional neural networks (CNNs) in the context of computer-aided abdominal and mediastinal lymph node detection and diagnosis (CAD) using CT images;  ... 
doi:10.1080/21681163.2019.1591722 fatcat:wehmcoib6rdvjdcxsnmhkiksfm

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Jeffrey Liu, Bino Varghese, Farzaneh Taravat, Liesl S. Eibschutz, Ali Gholamrezanezhad
2022 Diagnostics  
Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for  ...  While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide  ...  Multisource CT images acquired from 93 subjects who Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images Ukai et al. [10] Scientific Reports 2021  ... 
doi:10.3390/diagnostics12061351 pmid:35741161 pmcid:PMC9221728 fatcat:chuolsu2ufbfpnswaxca3mjonm

Automatic detection and segmentation of lumbar vertebra from X-ray images for compression fracture evaluation [article]

Kang Cheol Kim, Hyun Cheol Cho, Tae Jun Jang, Jong Mun Choi, Jin Keun Seo
2019 arXiv   pre-print
For compression fracture detection and evaluation, an automatic X-ray image segmentation technique that combines deep-learning and level-set methods is proposed.  ...  Automatic segmentation is much more difficult for X-ray images than for CT or MRI images because they contain overlapping shadows of thoracoabdominal structures including lungs, bowel gases, and other  ...  Various automated vertebral segmentation methods have been developed for use with medical imaging modalities, most commonly for CT and to a lesser degree for X-ray images.  ... 
arXiv:1904.07624v1 fatcat:7mvzfu2jm5huhbuq3hggyskgcq

The Role of Machine Learning in Spine Surgery: The Future Is Now

Michael Chang, Jose A. Canseco, Kristen J. Nicholson, Neil Patel, Alexander R. Vaccaro
2020 Frontiers in Surgery  
Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery,  ...  of examples gathered from the spine literature.  ...  In addition to spinal segmentation, significant strides have also been made in automated detection of vertebral compression and posterior element fractures, as well FIGURE 9 | Visual representation of  ... 
doi:10.3389/fsurg.2020.00054 pmid:32974382 pmcid:PMC7472375 fatcat:527af622krhbxbqx6ksybnoiu4

Spinal vertebrae localization and analysis on disproportionality in curvature using radiography—a comprehensive review

Joddat Fatima, Muhammad Usman Akram, Amina Jameel, Adeel Muzaffar Syed
2021 EURASIP Journal on Image and Video Processing  
In recent times, convolutional nnural Network (CNN) has taken the research to the next level, producing high-accuracy results.  ...  The spinal cord assists the overall communication network of the human anatomy through the brain.  ...  Recent literature In a study [33] , Korez et al. developed an automated supervised segmentation method for vertebral bodies with the help of 3D convolutional neural network (CNN).  ... 
doi:10.1186/s13640-021-00563-5 fatcat:khzxpnubxjdnto4zdcpbxqxvnu

Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features

Adela Arpitha, Lalitha Rangarajan
2021 International Journal of Cognitive Informatics and Natural Intelligence  
On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for  ...  The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans.  ...  ., 2018) proposed an automated method to detect and classify vertebral fractures from 3D CT lumbar spine images using convolution neural network.  ... 
doi:10.4018/ijcini.20211001.oa21 fatcat:67qexg4a2zcw7cyqktgff2plxy

Artificial intelligence and machine learning in spine research

Fabio Galbusera, Gloria Casaroli, Tito Bassani
2019 JOR Spine  
In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research.  ...  These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution  ...  In being developed for the detection of spine metastases on CT scans, which has been undertaken by using a classifier trained on a number of features extracted from the image of each single vertebra.  ... 
doi:10.1002/jsp2.1044 pmid:31463458 pmcid:PMC6686793 fatcat:2ahy3rxwxrdbbgofqugbfiy4ry

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs

Sebastiano Caprara, Fabio Carrillo, Jess G. Snedeker, Mazda Farshad, Marco Senteler
2021 Frontiers in Bioengineering and Biotechnology  
We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific  ...  The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical  ...  Gerber for the help with the FE simulations and convergence study, and Frédéric Cornaz and Jonas Widmer for providing the data of the pathological FSUs.  ... 
doi:10.3389/fbioe.2021.636953 pmid:33585436 pmcid:PMC7876284 fatcat:lfvxm7cdifhifdmxicxzpl6jg4

Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams

Simin Liu, Ming Feng, Tingting Qiao, Haidong Cai, Kele Xu, Xiaqing Yu, Wen Jiang, Zhongwei Lv, Yin Wang, Dan Li
2022 Cancer Management and Research  
A deep residual convolutional neural network with different structures was used to determine whether metastatic bone lesions existed, regions of lesions were automatically segmented.  ...  To develop an approach for automatically analyzing bone metastases (BMs) on bone scintigrams based on deep learning technology.  ...  Pi 13 reported an accuracy of 94.19% for automated diagnosis of BM based on multi-view bone scans using attention-augmented deep neural networks, and his datasets were from more than ten thousand of  ... 
doi:10.2147/cmar.s340114 pmid:35018121 pmcid:PMC8740774 fatcat:hjxtpaclyfcblcszzjcer2on2a

A Review on the Use of Artificial Intelligence in Spinal Diseases

Parisa Azimi, Taravat Yazdanian, Edward C. Benzel, Hossein Nayeb Aghaei, Shirzad Azhari, Sohrab Sadeghi, Ali Montazeri
2020 Asian Spine Journal  
," "deep learning," and "imaging."  ...  The search strategy was set as the combinations of the following keywords: "artificial neural networks," "spine," "back pain," "prognosis," "grading," "classification," "prediction," "segmentation," "biomechanics  ...  Acknowledgments The authors thank the staff of the Neurosurgery Unit at Imam-Hossain Hospital, Tehran, Iran.  ... 
doi:10.31616/asj.2020.0147 pmid:32326672 pmcid:PMC7435304 fatcat:cxdxp3jpurcgzp2hjne5mrj5qu

Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models

Malaika Mushtaq, Muhammad Usman Akram, Norah Saleh Alghamdi, Joddat Fatima, Rao Farhat Masood
2022 Sensors  
In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities.  ...  The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures.  ...  Conflicts of Interest: No conflict of interest is declared by the authors.  ... 
doi:10.3390/s22041547 pmid:35214448 pmcid:PMC8879729 fatcat:uccac7posbfc7jxvtadctgxkji

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis

Qiang Lin, Chuangui Cao, Tongtong Li, Zhengxing Man, Yongchun Cao, Haijun Wang
2021 BMC Medical Imaging  
Methods Focusing on the automated diagnosis of diseases with whole-body SPECT scintigraphic images, in this work, a self-defined convolutional neural network is developed to survey the presence or absence  ...  adversarial network techniques on the original SPECT imaging data.  ...  Supervised classification network dSPIC In this work, we self-define a deep SPECT Image Classification network (dSPIC) for automated diagnosis of diseases of concern.  ... 
doi:10.1186/s12880-021-00653-w pmid:34380441 pmcid:PMC8359584 fatcat:uc3pe2tturalpm7j4beajspvqm
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