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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/29086081/automated-radiology-report-summarization-using-an-open-source-natural-language-processing-pipeline
#1
Daniel J Goff, Thomas W Loehfelm
Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding...
October 30, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29086080/review-of-ubc-radiology-teaching-app
#2
REVIEW
Rebecca Spouge
No abstract text is available yet for this article.
October 30, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29079959/integrating-natural-language-processing-and-machine-learning-algorithms-to-categorize-oncologic-response-in-radiology-reports
#3
Po-Hao Chen, Hanna Zafar, Maya Galperin-Aizenberg, Tessa Cook
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer...
October 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29071591/app-review-management-guide-for-incidental-findings-on-ct-and-mri
#4
REVIEW
Mark D Kovacs, Philip F Burchett, Douglas H Sheafor
No abstract text is available yet for this article.
October 25, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29067570/an-efficient-pipeline-for-abdomen-segmentation-in-ct-images
#5
Hasan Koyuncu, Rahime Ceylan, Mesut Sivri, Hasan Erdogan
Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques...
October 24, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29047035/application-of-super-resolution-convolutional-neural-network-for-enhancing-image-resolution-in-chest-ct
#6
Kensuke Umehara, Junko Ota, Takayuki Ishida
In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image...
October 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29047034/quantitative-volumetric-k-means-cluster-segmentation-of-fibroglandular-tissue-and-skin-in-breast-mri
#7
Anton Niukkanen, Otso Arponen, Aki Nykänen, Amro Masarwah, Anna Sutela, Timo Liimatainen, Ritva Vanninen, Mazen Sudah
Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52...
October 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29047033/characterization-of-pulmonary-nodules-based-on-features-of-margin-sharpness-and-texture
#8
José Raniery Ferreira, Marcelo Costa Oliveira, Paulo Mazzoncini de Azevedo-Marques
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness...
October 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29047032/rethinking-skin-lesion-segmentation-in-a-convolutional-classifier
#9
Jack Burdick, Oge Marques, Janet Weinthal, Borko Furht
Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion...
October 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29043528/comparison-of-shallow-and-deep-learning-methods-on-classifying-the-regional-pattern-of-diffuse-lung-disease
#10
Guk Bae Kim, Kyu-Hwan Jung, Yeha Lee, Hyun-Jun Kim, Namkug Kim, Sanghoon Jun, Joon Beom Seo, David A Lynch
This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing)...
October 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28993897/quantitative-image-feature-engine-qife-an-open-source-modular-engine-for-3d-quantitative-feature-extraction-from-volumetric-medical-images
#11
Sebastian Echegaray, Shaimaa Bakr, Daniel L Rubin, Sandy Napel
The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output...
October 6, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28983851/a-deep-learning-system-for-fully-automated-peripherally-inserted-central-catheter-picc-tip-detection
#12
Hyunkwang Lee, Mohammad Mansouri, Shahein Tajmir, Michael H Lev, Synho Do
A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location...
October 5, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28971250/a-novel-texture-quantization-based-reversible-multiple-watermarking-scheme-applied-to-health-information-system
#13
Mousami Turuk, Ashwin Dhande
The recent innovations in information and communication technologies have appreciably changed the panorama of health information system (HIS). These advances provide new means to process, handle, and share medical images and also augment the medical image security issues in terms of confidentiality, reliability, and integrity. Digital watermarking has emerged as new era that offers acceptable solutions to the security issues in HIS. Texture is a significant feature to detect the embedding sites in an image, which further leads to substantial improvement in the robustness...
October 2, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28948384/electronic-medical-record-integration-for-streamlined-dxa-reporting
#14
Jason Wachsmann, Kyle Blain, Mathew Thompson, Solomon Cherian, Orhan K Oz, Travis Browning
Dual-energy X-ray absorptiometry (DXA) is the most frequently performed examination to assess bone mineral density in clinical practice. Aside from images and graphical displays, many numerical values are part of DXA reports. These values are typically manually entered into the formal report through the electronic medical record or PACS workstation. The process takes time and is prone to errors. Exporting the DXA numerical data via HL7 engine to the electronic medical record was proposed to improve reporting efficiency and accuracy...
September 25, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28932980/understanding-clinical-mammographic-breast-density-assessment-a-deep-learning-perspective
#15
Aly A Mohamed, Yahong Luo, Hong Peng, Rachel C Jankowitz, Shandong Wu
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view...
September 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28924878/differences-between-schizophrenic-and-normal-subjects-using-network-properties-from-fmri
#16
Youngoh Bae, Kunaraj Kumarasamy, Issa M Ali, Panagiotis Korfiatis, Zeynettin Akkus, Bradley J Erickson
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects...
September 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28924815/automatic-determination-of-the-need-for-intravenous-contrast-in-musculoskeletal-mri-examinations-using-ibm-watson-s-natural-language-processing-algorithm
#17
Hari Trivedi, Joseph Mesterhazy, Benjamin Laguna, Thienkhai Vu, Jae Ho Sohn
Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors...
September 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28884381/development-of-a-computer-aided-differential-diagnosis-system-to-distinguish-between-usual-interstitial-pneumonia-and-non-specific-interstitial-pneumonia-using-texture-and-shape-based-hierarchical-classifiers-on-hrct-images
#18
SangHoon Jun, BeomHee Park, Joon Beom Seo, SangMin Lee, Namkug Kim
A computer-aided differential diagnosis (CADD) system that distinguishes between usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) using high-resolution computed tomography (HRCT) images was developed, and its results compared against the decision of a radiologist. Six local interstitial lung disease patterns in the images were determined, and 900 typical regions of interest were marked by an experienced radiologist. A support vector machine classifier was used to train and label the regions of interest of the lung parenchyma based on the texture and shape characteristics...
September 7, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28842816/single-center-experience-implementing-the-loinc-rsna-radiology-playbook-for-adult-abdomen-pelvis-ct-and-mr-procedures-using-a-semi-automated-method
#19
Ranjit S Sandhu, James Shin, Kenneth C Wang, George Shih
The LOINC-RSNA Radiology Playbook represents the future direction of standardization for radiology procedure names. We developed a software solution ("RadMatch") utilizing Python 2.7 and FuzzyWuzzy, an open-source fuzzy string matching algorithm created by SeatGeek, to implement the LOINC-RSNA Radiology Playbook for adult abdomen and pelvis CT and MR procedures performed at our institution. Execution of this semi-automated method resulted in the assignment of appropriate LOINC numbers to 86% of local CT procedures...
August 25, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28840386/medical-image-retrieval-using-multi-texton-assignment
#20
Qiling Tang, Jirong Yang, Xianfu Xia
In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation...
August 24, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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