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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/28695342/thyroid-nodule-classification-in-ultrasound-images-by-fine-tuning-deep-convolutional-neural-network
#1
Jianning Chi, Ekta Walia, Paul Babyn, Jimmy Wang, Gary Groot, Mark Eramian
With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts...
July 10, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28685320/can-laws-be-a-potential-pet-image-texture-analysis-approach-for-evaluation-of-tumor-heterogeneity-and-histopathological-characteristics-in-nsclc
#2
Seyhan Karacavus, Bülent Yılmaz, Arzu Tasdemir, Ömer Kayaaltı, Eser Kaya, Semra İçer, Oguzhan Ayyıldız
We investigated the association between the textural features obtained from (18)F-FDG images, metabolic parameters (SUVmax, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters...
July 6, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28685319/workflow-for-visualization-of-neuroimaging-data-with-an-augmented-reality-device
#3
Christof Karmonik, Timothy B Boone, Rose Khavari
Commercial availability of three-dimensional (3D) augmented reality (AR) devices has increased interest in using this novel technology for visualizing neuroimaging data. Here, a technical workflow and algorithm for importing 3D surface-based segmentations derived from magnetic resonance imaging data into a head-mounted AR device is presented and illustrated on selected examples: the pial cortical surface of the human brain, fMRI BOLD maps, reconstructed white matter tracts, and a brain network of functional connectivity...
July 6, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28681097/characterizing-diagnostic-search-patterns-in-digital-breast-pathology-scanners-and-drillers
#4
Ezgi Mercan, Linda G Shapiro, Tad T Brunyé, Donald L Weaver, Joann G Elmore
Following a baseline demographic survey, 87 pathologists interpreted 240 digital whole slide images of breast biopsy specimens representing a range of diagnostic categories from benign to atypia, ductal carcinoma in situ, and invasive cancer. A web-based viewer recorded pathologists' behaviors while interpreting a subset of 60 randomly selected and randomly ordered slides. To characterize diagnostic search patterns, we used the viewport location, time stamp, and zoom level data to calculate four variables: average zoom level, maximum zoom level, zoom level variance, and scanning percentage...
July 5, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28664448/impact-of-a-health-information-technology-intervention-on-the-follow-up-management-of-pulmonary-nodules
#5
Ronilda Lacson, Sonali Desai, Adam Landman, Randall Proctor, Siobhan Sumption, Ramin Khorasani
Lung cancer is the leading cause of cancer deaths in the USA. The most common abnormalities suspicious for lung cancer on CT scan include pulmonary nodules. Recommendations to improve care for patients with pulmonary nodules require follow-up management. However, transitions in care, especially for patients undergoing transitions to ambulatory care sites from the emergency department (ED) and inpatient settings, can exacerbate failures in follow-up testing and compromise patient safety. We evaluate the impact of a discharge module that includes follow-up recommendations for further management of pulmonary nodules on the study outcome and follow-up management of patients with pulmonary nodules within 1 year after discharge...
June 29, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28656455/malignancy-detection-on-mammography-using-dual-deep-convolutional-neural-networks-and-genetically-discovered-false-color-input-enhancement
#6
Philip Teare, Michael Fishman, Oshra Benzaquen, Eyal Toledano, Eldad Elnekave
Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers...
June 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28653123/pixel-level-deep-segmentation-artificial-intelligence-quantifies-muscle-on-computed-tomography-for-body-morphometric-analysis
#7
Hyunkwang Lee, Fabian M Troschel, Shahein Tajmir, Georg Fuchs, Julia Mario, Florian J Fintelmann, Synho Do
Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an "eyeball test" to assess whether patients will tolerate major surgery or chemotherapy, "eyeballing" is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts...
June 26, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28653122/machine-learning-discovering-the-future-of-medical-imaging
#8
EDITORIAL
Bradley J Erickson
No abstract text is available yet for this article.
June 26, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28639187/radiology-toolbox-pro-app-review
#9
EDITORIAL
George Rahmani
No abstract text is available yet for this article.
June 21, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28639186/computerized-prediction-of-radiological-observations-based-on-quantitative-feature-analysis-initial-experience-in-liver-lesions
#10
Imon Banerjee, Christopher F Beaulieu, Daniel L Rubin
We propose a computerized framework that, given a region of interest (ROI) circumscribing a lesion, not only predicts radiological observations related to the lesion characteristics with 83.2% average prediction accuracy but also derives explicit association between low-level imaging features and high-level semantic terms by exploiting their statistical correlation. Such direct association between semantic concepts and low-level imaging features can be leveraged to build a powerful annotation system for radiological images that not only allows the computer to infer the semantics from diverse medical images and run automatic reasoning for making diagnostic decision but also provides "human-interpretable explanation" of the system output to facilitate better end user understanding of computer-based diagnostic decisions...
June 21, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28639185/the-use-of-mobile-apps-by-radiology-journals
#11
LETTER
George Rahmani, Peter A McCarthy
No abstract text is available yet for this article.
June 21, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28623558/a-method-to-recognize-anatomical-site-and-image-acquisition-view-in-x-ray-images
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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