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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/29980960/a-decision-support-tool-for-renal-mass-classification
#1
Gautam Kunapuli, Bino A Varghese, Priya Ganapathy, Bhushan Desai, Steven Cen, Manju Aron, Inderbir Gill, Vinay Duddalwar
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists...
July 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29968111/app-review-series-cardiac-imaging-planes-1-2-3
#2
REVIEW
Ryan Brunetti, Andrew Choi
No abstract text is available yet for this article.
July 2, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29968110/app-review-series-ctisus-ipearls
#3
REVIEW
Justin Buro
Thousands of "pearls" or facts related to radiology or imaging are accessible through a categorically organized user interface. Pearls are divided up into anatomical regions, as well as non-organ-specific categories. Information is easily accessible with an ad-free interface matched with full offline capability. User interface leaves much to be desired as it appears dated and difficult to navigate at first. Pearls are not easily searchable within the application and unexperienced users may find it difficult to quickly answer targeted questions using this application alone...
July 2, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29968109/modeling-human-perception-of-image-quality
#4
Oleg S Pianykh, Ksenia Pospelova, Nick H Kamboj
Humans can determine image quality instantly and intuitively, but the mechanism of human perception of image quality is unknown. The purpose of this work was to identify the most important quantitative metrics responsible for the human perception of digital image quality. Digital images from two different datasets-CT tomography (MedSet) and scenic photographs of trees (TreeSet)-were presented in random pairs to unbiased human viewers. The observers were then asked to select the best-quality image from each image pair...
July 2, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29956010/touch-surgery-a-twenty-first-century-platform-for-surgical-training
#5
REVIEW
Ari Gilad Mandler
This manuscript reviews Touch Surgery, a novel online platform geared towards innovating professional training for surgical procedures. In other industries, such as aviation, simulation has already been shown to reduce costs and improve outcomes in crisis (JETS 3(4):348-352, 2010). Studies involving simulation-based learning in healthcare similarly indicate the potential for reducing errors through skill acquisition and cognitive retention (Int J Oral Maxillofac Surg 46:211, 2017). Cohort studies have shown improved performance among simulator-trained medical students in comparison to those with traditional ward training (Med Teach 9:53-57, 1987)...
June 28, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29915942/3d-segmentation-algorithms-for-computerized-tomographic-imaging-a-systematic-literature-review
#6
REVIEW
L E Carvalho, A C Sobieranski, A von Wangenheim
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description...
June 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29907888/reengineering-workflow-for-curation-of-dicom-datasets
#7
William Bennett, Kirk Smith, Quasar Jarosz, Tracy Nolan, Walter Bosch
Reusable, publicly available data is a pillar of open science and rapid advancement of cancer imaging research. Sharing data from completed research studies not only saves research dollars required to collect data, but also helps insure that studies are both replicable and reproducible. The Cancer Imaging Archive (TCIA) is a global shared repository for imaging data related to cancer. Insuring the consistency, scientific utility, and anonymity of data stored in TCIA is of utmost importance. As the rate of submission to TCIA has been increasing, both in volume and complexity of DICOM objects stored, the process of curation of collections has become a bottleneck in acquisition of data...
June 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29948436/laterality-classification-of-fundus-images-using-interpretable-deep-neural-network
#8
Yeonwoo Jang, Jaemin Son, Kyu Hyung Park, Sang Jun Park, Kyu-Hwan Jung
In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images)...
June 12, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29873011/pattern-recognition-and-size-prediction-of-microcalcification-based-on-physical-characteristics-by-using-digital-mammogram-images
#9
G R Jothilakshmi, Arun Raaza, V Rajendran, Y Sreenivasa Varma, R Guru Nirmal Raj
Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics...
June 5, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29869010/of-mice-and-roentgen-radiologist-satisfaction-with-a-non-conventional-13-button-mouse-one-institution-s-experience
#10
Kevin Denton, Irfanullah Haider, Jacqueline Hill, Suzanne L Hunt, Ryan Ash
Increasing radiologic exam volume and complexity necessitates leveraging advanced hardware solutions to optimize workflow efficiency. We evaluated radiologist satisfaction of a programmable 13-button non-conventional mouse compared to a conventional three-button mouse in daily interpretation workflow following a brief 2-day trial period. A prospective study was conducted with radiology staff and residents in a tertiary care center from 2015 to 2016. A survey was distributed prior to and after a tutorial and a 2-day non-conventional mouse trial period...
June 4, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29796972/three-dimensional-surface-point-cloud-ultrasound-for-better-understanding-and-transmission-of-ultrasound-scan-information
#11
Joseph Nathaniel Stember
Ultrasound is notoriously plagued by high user dependence. There is a steep drop-off in information in going from what the sonographer sees during image acquisition and what the interpreting radiologist is able to view at the reading station. One countermeasure is probe localization and tracking. Current implementations are too difficult and expensive to use and/or do not provide adequate detail and perspective. The aim of this work was to demonstrate that a protocol combining surface three-dimensional photographic imaging with traditional ultrasound images may be a solution to the problem of probe localization, this approach being termed surface point cloud ultrasound (SPC-US)...
May 23, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29777325/developing-an-interactive-data-visualization-tool-to-assess-the-impact-of-decision-support-on-clinical-operations
#12
Timothy C Huber, Arun Krishnaraj, Dayna Monaghan, Cree M Gaskin
Due to mandates from recent legislation, clinical decision support (CDS) software is being adopted by radiology practices across the country. This software provides imaging study decision support for referring providers at the point of order entry. CDS systems produce a large volume of data, providing opportunities for research and quality improvement. In order to better visualize and analyze trends in this data, an interactive data visualization dashboard was created using a commercially available data visualization platform...
May 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29777324/a-dependable-massive-storage-service-for-medical-imaging
#13
Marco Antonio Núñez-Gaona, Ricardo Marcelín-Jiménez, Josefina Gutiérrez-Martínez, Heriberto Aguirre-Meneses, José Luis Gonzalez-Compean
We present the construction of Babel, a distributed storage system that meets stringent requirements on dependability, availability, and scalability. Together with Babel, we developed an application that uses our system to store medical images. Accordingly, we show the feasibility of our proposal to provide an alternative solution for massive scientific storage and describe the software architecture style that manages the DICOM images life cycle, utilizing Babel like a virtual local storage component for a picture archiving and communication system (PACS-Babel Interface)...
May 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29777323/andriod-device-based-cervical-cancer-screening-for-resource-poor-settings
#14
Vidya Kudva, Keerthana Prasad, Shyamala Guruvare
Visual inspection with acetic acid (VIA) is an effective, affordable and simple test for cervical cancer screening in resource-poor settings. But considerable expertise is needed to differentiate cancerous lesions from normal lesions, which is lacking in developing countries. Many studies have attempted automation of cervical cancer detection from cervix images acquired during the VIA process. These studies used images acquired through colposcopy or cervicography. However, colposcopy is expensive and hence is not feasible as a screening tool in resource-poor settings...
May 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29748852/dicomweb%C3%A2-background-and-application-of-the-web-standard-for-medical-imaging
#15
Brad W Genereaux, Donald K Dennison, Kinson Ho, Robert Horn, Elliot Lewis Silver, Kevin O'Donnell, Charles E Kahn
This paper describes why and how DICOM, the standard that has been the basis for medical imaging interoperability around the world for several decades, has been extended into a full web technology-based standard, DICOMweb. At the turn of the century, healthcare embraced information technology, which created new problems and new opportunities for the medical imaging industry; at the same time, web technologies matured and began serving other domains well. This paper describes DICOMweb, how it extended the DICOM standard, and how DICOMweb can be applied to problems facing healthcare applications to address workflow and the changing healthcare climate...
May 10, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29748851/a-platform-for-innovation-and-standards-evaluation-a-case-study-from-the-openmrs-open-source-radiology-information-system
#16
Judy W Gichoya, Marc Kohli, Larry Ivange, Teri S Schmidt, Saptarshi Purkayastha
Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology...
May 10, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29744689/effectiveness-of-an-e-learning-platform-for-image-interpretation-education-of-medical-staff-and-students
#17
Akio Ogura, Norio Hayashi, Tohru Negishi, Haruyuki Watanabe
Medical staff must be able to perform accurate initial interpretations of radiography to prevent diagnostic errors. Education in medical image interpretation is an ongoing need that is addressed by text-based and e-learning platforms. The effectiveness of these methods has been previously reported. Here, we describe the effectiveness of an e-learning platform used for medical image interpretation education. Ten third-year medical students without previous experience in chest radiography interpretation were provided with e-learning instructions...
May 9, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29740715/container-based-clinical-solutions-for-portable-and-reproducible-image-analysis
#18
Jordan Matelsky, Gregory Kiar, Erik Johnson, Corban Rivera, Michael Toma, William Gray-Roncal
Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis...
May 8, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29736781/combination-of-rs-fmri-and-smri-data-to-discriminate-autism-spectrum-disorders-in-young-children-using-deep-belief-network
#19
Maryam Akhavan Aghdam, Arash Sharifi, Mir Mohsen Pedram
In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets...
May 7, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29725966/iot-in-radiology-using-raspberry-pi-to-automatically-log-telephone-calls-in-the-reading-room
#20
REVIEW
Po-Hao Chen, Nathan Cross
The work environment for medical imaging such as distractions, ergonomics, distance, temperature, humidity, and lighting conditions generates a paucity of data and is difficult to analyze. The emergence of Internet of Things (IoT) with decreasing cost of single-board computers like Raspberry Pi makes creating customized hardware to collect data from the clinical environment within the reach of a clinical imaging informaticist. This article will walk the reader through a series of basic project using a variety sensors and devices in conjunction with a Pi to gather data, culminating in a complex example designed to automatically detect and log telephone calls...
May 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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