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Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology

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https://www.readbyqxmd.com/read/28623558/a-method-to-recognize-anatomical-site-and-image-acquisition-view-in-x-ray-images
#1
Xiao Chang, Thomas Mazur, H Harold Li, Deshan Yang
A method was developed to recognize anatomical site and image acquisition view automatically in 2D X-ray images that are used in image-guided radiation therapy. The purpose is to enable site and view dependent automation and optimization in the image processing tasks including 2D-2D image registration, 2D image contrast enhancement, and independent treatment site confirmation. The X-ray images for 180 patients of six disease sites (the brain, head-neck, breast, lung, abdomen, and pelvis) were included in this study with 30 patients each site and two images of orthogonal views each patient...
June 16, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28623557/proving-value-in-radiology-experience-developing-and-implementing-a-shareable-open-source-registry-platform-driven-by-radiology-workflow
#2
Judy Wawira Gichoya, Marc D Kohli, Paul Haste, Elizabeth Mills Abigail, Matthew S Johnson
Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry...
June 16, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28616636/a-semi-automated-approach-to-improve-the-efficiency-of-medical-imaging-segmentation-for-haptic-rendering
#3
Pat Banerjee, Mengqi Hu, Rahul Kannan, Srinivasan Krishnaswamy
The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering...
June 14, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28600641/predicting-deletion-of-chromosomal-arms-1p-19q-in-low-grade-gliomas-from-mr-images-using-machine-intelligence
#4
Zeynettin Akkus, Issa Ali, Jiří Sedlář, Jay P Agrawal, Ian F Parney, Caterina Giannini, Bradley J Erickson
Several studies have linked codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression-free survival. Hence, predicting 1p/19q status is crucial for effective treatment planning of LGG. In this study, we predict the 1p/19q status from MR images using convolutional neural networks (CNN), which could be a non-invasive alternative to surgical biopsy and histopathological analysis. Our method consists of three main steps: image registration, tumor segmentation, and classification of 1p/19q status using CNN...
June 9, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28600640/deep-convolutional-neural-networks-for-endotracheal-tube-position-and-x-ray-image-classification-challenges-and-opportunities
#5
Paras Lakhani
The goal of this study is to evaluate the efficacy of deep convolutional neural networks (DCNNs) in differentiating subtle, intermediate, and more obvious image differences in radiography. Three different datasets were created, which included presence/absence of the endotracheal (ET) tube (n = 300), low/normal position of the ET tube (n = 300), and chest/abdominal radiographs (n = 120). The datasets were split into training, validation, and test. Both untrained and pre-trained deep neural networks were employed, including AlexNet and GoogLeNet classifiers, using the Caffe framework...
June 9, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28585063/enabling-real-time-volume-rendering-of-functional-magnetic-resonance-imaging-on-an-ios-device
#6
Joseph Holub, Eliot Winer
Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity...
June 5, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28577131/deep-learning-for-brain-mri-segmentation-state-of-the-art-and-future-directions
#7
REVIEW
Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L Rubin, Bradley J Erickson
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI...
June 2, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28560509/evaluation-of-coronary-artery-disease-and-coronary-anomalies-with-a-handheld-smartphone
#8
Cheng Ting Lin, Stefan Loy Zimmerman, Linda C Chu, John Eng, Elliot K Fishman
The purpose of this study was to determine the diagnostic accuracy of an iPhone for evaluation of the coronary arteries on coronary CT angiography (CTA) in comparison to a standard clinical workstation. Fifty coronary CTA exams were selected to include a range of normal and abnormal cases including both coronary artery disease (CAD) of varying severity and coronary artery anomalies. Two cardiac radiologists reviewed each exam on a standard clinical workstation initially and then on an iPhone 6 after a washout period...
May 30, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28560508/does-the-use-of-a-checklist-help-medical-students-in-the-detection-of-abnormalities-on-a-chest-radiograph
#9
Ellen M Kok, Abdelrazek Abed, Simon G F Robben
The interpretation of chest radiographs is a complex task that is prone to diagnostic error, especially for medical students. The aim of this study is to investigate the extent to which medical students benefit from the use of a checklist regarding the detection of abnormalities on a chest radiograph. We developed a checklist based on literature and interviews with experienced thorax radiologists. Forty medical students in the clinical phase assessed 18 chest radiographs during a computer test, either with (n = 20) or without (n = 20) the checklist...
May 30, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28550374/performance-of-an-artificial-multi-observer-deep-neural-network-for-fully-automated-segmentation-of-polycystic-kidneys
#10
Timothy L Kline, Panagiotis Korfiatis, Marie E Edwards, Jaime D Blais, Frank S Czerwiec, Peter C Harris, Bernard F King, Vicente E Torres, Bradley J Erickson
Deep learning techniques are being rapidly applied to medical imaging tasks-from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys...
May 26, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28526968/computer-aided-diagnosis-of-lung-nodules-in-computed-tomography-by-using-phylogenetic-diversity-genetic-algorithm-and-svm
#11
Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes...
May 19, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28523622/app-review-series-epocrates
#12
EDITORIAL
Shelly Bhanot, Arjun Sharma
No abstract text is available yet for this article.
May 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28516233/medical-image-data-and-datasets-in-the-era-of-machine-learning-whitepaper-from-the-2016-c-mimi-meeting-dataset-session
#13
Marc D Kohli, Ronald M Summers, J Raymond Geis
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities...
May 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28484920/can-an-offsite-expert-remotely-evaluate-the-visual-estimation-of-ejection-fraction-via-a-social-network-video-call
#14
Changsun Kim, Jin Hur, Bo Seung Kang, Hyuk Joong Choi, Jeong-Hun Shin, Tae-Hyung Kim, Jae Ho Chung
We aimed to investigate whether an offsite expert could effectively evaluate visually estimated ejection fraction (EF) while watching and guiding the echocardiographic procedure of an onsite novice practitioner using a social network video call. Sixty patients presenting to the intensive care unit and requiring echocardiography between October and November 2016 were included. Sixty novice sonographers without any previous experience of echocardiography participated. Prior to the procedure, the onsite cardiologist completed the echocardiography and determined the EF using the modified Simpson's method (reference value)...
May 8, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28484919/medical-image-tamper-detection-based-on-passive-image-authentication
#15
Guzin Ulutas, Arda Ustubioglu, Beste Ustubioglu, Vasif V Nabiyev, Mustafa Ulutas
Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission...
May 8, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28484918/simplified-readability-metric-drives-improvement-of-radiology-reports-an-experiment-on-ultrasound-reports-at-a-pediatric-hospital
#16
Wei Chen, Claire Durkin, Yungui Huang, Brent Adler, Steve Rust, Simon Lin
Highly complex medical documents, including ultrasound reports, are greatly mismatched with patient literacy levels. While improving radiology reports for readability is a longstanding concern, few articles objectively measure the effectiveness of physician training for readability improvement. We hypothesized that writing styles may be evaluated using an objective two-dimensional measure and writing training could improve the writing styles of radiologists. To test it, a simplified "grade vs. length" readability metric is developed based on results from factor analysis of ten readability metrics applied to more than 500,000 radiology reports...
May 8, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28455638/radiologist-digital-workspace-use-and-preference-a-survey-based-study
#17
Arjun Sharma, Kenneth Wang, Eliot Siegel
Literature regarding the heterogeneity of and preferences for radiology workstation design-and, in particular, the digital workspace of the radiology workstation-is scant. The purpose of this study was to determine the nature of the digital environments across the specialty and the degree of satisfaction users associated with the particular facets of those environments. A survey was sent to the membership of the Association of University Radiologists in February 2015. The survey comprised 10 questions establishing demographics, current typical workstation setup, perceived satisfaction with that setup, and preferences for potential altered setups...
April 28, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28429195/computer-assisted-diagnosis-system-for-breast-cancer-in-computed-tomography-laser-mammography-ctlm
#18
Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M Iqbal Saripan, Abdul Rahman Ramli
Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images...
April 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28405834/feasibility-study-of-a-generalized-framework-for-developing-computer-aided-detection-systems-a-new-paradigm
#19
Mitsutaka Nemoto, Naoto Hayashi, Shouhei Hanaoka, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa
We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance...
April 12, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28378032/computer-based-radiographic-quantification-of-joint-space-narrowing-progression-using-sequential-hand-radiographs-validation-study-in-rheumatoid-arthritis-patients-from-multiple-institutions
#20
Shota Ichikawa, Tamotsu Kamishima, Kenneth Sutherland, Jun Fukae, Kou Katayama, Yuko Aoki, Takanobu Okubo, Taichi Okino, Takahiko Kaneda, Satoshi Takagi, Kazuhide Tanimura
We have developed a refined computer-based method to detect joint space narrowing (JSN) progression with the joint space narrowing progression index (JSNPI) by superimposing sequential hand radiographs. The purpose of this study is to assess the validity of a computer-based method using images obtained from multiple institutions in rheumatoid arthritis (RA) patients. Sequential hand radiographs of 42 patients (37 females and 5 males) with RA from two institutions were analyzed by a computer-based method and visual scoring systems as a standard of reference...
April 4, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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