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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/30402671/suspicious-lesion-segmentation-on-brain-mammograms-and-breast-mr-images-using-new-optimized-spatial-feature-based-super-pixel-fuzzy-c-means-clustering
#1
S N Kumar, A Lenin Fred, P Sebastin Varghese
Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions...
November 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30402670/full-dose-pet-image-estimation-from-low-dose-pet-image-using-deep-learning-a-pilot-study
#2
Sydney Kaplan, Yang-Ming Zhu
Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model that takes specific image features into account in the loss function to denoise low-dose PET image slices and estimate their full-dose image quality equivalent...
November 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30402669/multi-objective-parameter-auto-tuning-for-tissue-image-segmentation-workflows
#3
Luis F R Taveira, Tahsin Kurc, Alba C M A Melo, Jun Kong, Erich Bremer, Joel H Saltz, George Teodoro
We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. The shape, size, and texture features of nuclei in tissue are important biomarkers for disease prognosis, and accurate computation of these features depends on accurate delineation of boundaries of nuclei. Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance...
November 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30402668/integrating-active-learning-and-transfer-learning-for-carotid-intima-media-thickness-video-interpretation
#4
Zongwei Zhou, Jae Shin, Ruibin Feng, R Todd Hurst, Christopher B Kendall, Jianming Liang
Cardiovascular disease (CVD) is the number one killer in the USA, yet it is largely preventable (World Health Organization 2011). To prevent CVD, carotid intima-media thickness (CIMT) imaging, a noninvasive ultrasonography method, has proven to be clinically valuable in identifying at-risk persons before adverse events. Researchers are developing systems to automate CIMT video interpretation based on deep learning, but such efforts are impeded by the lack of large annotated CIMT video datasets. CIMT video annotation is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible...
November 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30402667/integrating-wikipedia-articles-and-images-into-an-information-resource-for-radiology-patients
#5
Teresa Martin-Carreras, Charles E Kahn
Wikipedia-an open-access online encyclopedia-contains a large number of medically relevant articles and images that may help supplement glossaries of radiology terms. We sought to determine the extent to which concepts from a large online radiology glossary developed as part of the Patient-Oriented Radiology Reporter (PORTER) initiative could be mapped to relevant Wikipedia web pages and images using automated or semi-automated approaches. The glossary included 4090 concepts with their definitions; the concept's preferred name and lexical variants, such as plurals, adjectival forms, synonyms, and abbreviations, yielded a total of 13,030 terms...
November 6, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30374655/a-survey-of-imaging-informatics-fellowships-and-their-curricula-current-state-assessment
#6
Brianna L Vey, T S Cook, P Nagy, R J Bruce, R W Filice, K C Wang, N M Safdar
In a 2016 survey of imaging informatics ("II") fellowship graduates, the surveyed fellowship graduates expressed the "opinion that II fellowships needed further formalization and standardization" Liao et al. (J Digit Imaging, 2016). This, coupled with the fact that the original published "standardized" curriculum is about 15 years out of date in our rapidly changing systems, suggests an opportunity for curriculum improvement. Before agreeing on improved structural and content suggestions for fellowships, we completed a current-state assessment of how each fellowship organizes its education and what requirements each have for fellowship completion...
October 29, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30367308/bone-cancer-assessment-and-destruction-pattern-analysis-in-long-bone-x-ray-image
#7
Oishila Bandyopadhyay, Arindam Biswas, Bhargab B Bhattacharya
Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region...
October 26, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30361936/prior-to-initiation-of-chemotherapy-can-we-predict-breast-tumor-response-deep-learning-convolutional-neural-networks-approach-using-a-breast-mri-tumor-dataset
#8
Richard Ha, Christine Chin, Jenika Karcich, Michael Z Liu, Peter Chang, Simukayi Mutasa, Eduardo Pascual Van Sant, Ralph T Wynn, Eileen Connolly, Sachin Jambawalikar
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data...
October 25, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30361935/content-based-image-retrieval-system-for-pulmonary-nodules-using-optimal-feature-sets-and-class-membership-based-retrieval
#9
Shrikant A Mehre, Ashis Kumar Dhara, Mandeep Garg, Naveen Kalra, Niranjan Khandelwal, Sudipta Mukhopadhyay
Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR)...
October 25, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30353411/active-learning-of-the-hl7-medical-standard
#10
Rita Noumeir
Health Level Seven (HL7®) is a standard for exchanging information between medical information systems. It is widely deployed and covers the exchange of information in several functional domains. It is very important and crucial to achieve interoperability in healthcare. HL7 competences are needed by all professionals touching information technology in healthcare. However, learning the standard has always been long and difficult due to its large breadth as well as to large and complex documentation. In this paper, we describe an innovative active learning approach based on solving problems from real clinical scenarios to learn the HL7 standard, quickly...
October 23, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30350007/transcending-dimensions-a-comparative-analysis-of-cloaca-imaging-in-advancing-the-surgeon-s-understanding-of-complex-anatomy
#11
Alessandra C Gasior, Carlos Reck, Victoria Lane, Richard J Wood, Jeremy Patterson, Robert Strouse, Simon Lin, Jennifer Cooper, D Gregory Bates, Marc A Levitt
Surgeons have a steep learning capacity to understand 2-D images provided by conventional cloacagrams. Imaging advances now allow for 3-D reconstruction and 3-D models; but no evaluation of the value of these techniques exists in the literature. Therefore, we sought to determine if advances in 3-D imaging would benefit surgeons, lead to accelerated learning, and improve understanding for operative planning of a cloaca reconstruction. Questionnaires were used to assess the understanding of 2-D and 3-D images by pediatric surgical faculty and trainees...
October 22, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30350006/radiomics-in-rayplus-a-web-based-tool-for-texture-analysis-in-medical-images
#12
Rong Yuan, Shuyue Shi, Junhui Chen, Guanxun Cheng
Radiomics has been shown to have considerable potential and value in quantifying the tumor phenotype and predicting the treatment response. In most scenarios, the commercial and open-source software programs are available for quantitative analysis in medical images to streamline radiomics research. However, at this stage, most of these programs are local applications and require users to have experience in programming and software engineering, which clinicians usually do not have. Therefore, in this article, a web-based tool was proposed to flexibly support radiomics research workflow tasks...
October 22, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30338478/comprehensive-word-level-classification-of-screening-mammography-reports-using-a-neural-network-sequence-labeling-approach
#13
Ryan G Short, John Bralich, Dave Bogaty, Nicholas T Befera
Radiology reports contain a large amount of potentially valuable unstructured data. Recently, neural networks have been employed to perform classification of radiology reports over a few classes at the document level. The success of neural networks in sequence-labeling problems such as named entity recognition and part of speech tagging suggests that they could be used to classify radiology report text with greater granularity. We employed a neural network architecture to comprehensively classify mammography report text at the word level using a sequence labeling approach...
October 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30338477/automatic-lumbar-mri-detection-and-identification-based-on-deep-learning
#14
Yujing Zhou, Yuan Liu, Qian Chen, Guohua Gu, Xiubao Sui
The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image...
October 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30338476/implementation-and-benefits-of-a-vendor-neutral-archive-and-enterprise-imaging-management-system-in-an-integrated-delivery-network
#15
Chen Sirota-Cohen, Beverly Rosipko, Daniel Forsberg, Jeffrey L Sunshine
The use of digital imaging has substantially grown in recent decades, in traditional services, new specialties, and departments. The need to share these data among departments and caregivers necessitated central archiving systems that are able to communicate with various viewing applications and electronic medical records. This promoted the development of modern vendor neutral archive (VNA) systems. The need to aggregate and share imaging data from various departments promoted the development of enterprise-imaging (EI) solutions that replace departmental silos of data with central healthcare enterprise databases...
October 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30324429/impact-of-data-presentation-on-physician-performance-utilizing-artificial-intelligence-based-computer-aided-diagnosis-and-decision-support-systems
#16
L Barinov, A Jairaj, M Becker, S Seymour, E Lee, A Schram, E Lane, A Goldszal, D Quigley, L Paster
Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions sent for biopsy (PPV3) and specificity. A recent study demonstrated that incorporating an AI-based decision support (DS) system into US image analysis could help improve US diagnostic performance. While the DS system is promising, its efficacy in terms of its impact also needs to be measured when integrated into existing clinical workflows...
October 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30324428/accurate-age-determination-for-adolescents-using-magnetic-resonance-imaging-of-the-hand-and-wrist-with-an-artificial-neural-network-based-approach
#17
Fuk Hay Tang, Jasmine L C Chan, Bill K L Chan
This study proposes an accurate method in assessing chronological age of the adolescents using a machine learning approach using MRI images. We also examined the value of MRI with Tanner-Whitehouse 3 (TW3) method in assessing skeletal maturity. Seventy-nine 12-17-year-old healthy Hong Kong Chinese adolescents were recruited. The left hand and wrist region were scanned by a dedicated skeletal MRI scanner. T1-weighted three-dimensional coronal view images for the left hand and wrist region were acquired. Independent maturity indicators such as subject body height, body weight, bone marrow composition intensity quantified by MRI, and TW3 skeletal age were included for artificial neural network (ANN) analysis...
October 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30306418/applying-densely-connected-convolutional-neural-networks-for-staging-osteoarthritis-severity-from-plain-radiographs
#18
Berk Norman, Valentina Pedoia, Adam Noworolski, Thomas M Link, Sharmila Majumdar
Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs of OA, 2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading is widely used for clinical assessment and diagnosis of OA, usually on a high volume of radiographs, making its automation highly relevant. We propose a fully automated algorithm for the detection of OA using KL gradings with a state-of-the-art neural network. Four thousand four hundred ninety bilateral PA fixed-flexion knee radiographs were collected from the Osteoarthritis Initiative dataset (age = 61...
October 10, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30298437/the-role-of-the-integrated-digital-radiology-system-in-assessing-the-impact-of-patient-load-on-emergency-computed-tomography-ct-efficiency
#19
Suzanne O'Hagan, Carl J Lombard, Richard D Pitcher
Time-critical management is of particular significance in the trauma and emergency setting, where intervals from patient arrival to diagnostic imaging and from imaging to radiology report are key determinants of outcome. This study, based in the Trauma and Emergency Unit of a large, tertiary-level African hospital with a fully digital radiology department, assessed the impact of increased workload on computerised tomography (CT) efficiency. Sequential, customised searches of the institutional radiology information system (RIS) were conducted to define two weekends in 2016 with the lowest and highest emergency CT workloads, respectively...
October 8, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30298436/utilization-of-structured-reporting-to-monitor-outcomes-of-doppler-ultrasound-performed-for-deep-vein-thrombosis
#20
Travis Browning, Sura Giri, Ron Peshock, Julia Fielding
Determining the clinical impact of imaging exams at the enterprise level is problematic, as radiology reports historically have been created with the content meant primarily for the referring provider. Structured reporting can establish the foundation for enterprise monitoring of imaging outcomes without manual review providing the framework for assessment of utilization and quality. Ultrasound (US) for deep vein thrombosis evaluation (DVT) is an ideal testbed for assessing this functionality. The system standard template for Doppler US for extremity venous evaluation for DVT was updated with a discrete fixed picklist of impression options and implemented system wide...
October 8, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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