A Marker-Less Registration Approach for Mixed Reality –Aided Maxillofacial Surgery: a Pilot Evaluation
AbstractAs of common routine in tumor resections, surgeons rely on local examinations of the removed tissues and on the swiftly made microscopy findings of the pathologist, which are based on intraoperatively taken tissue probes. This approach may imply an extended duration of the operation, increased effort for the medical staff, and longer occupancy of the operating room (OR). Mixed reality technologies, and particularly augmented reality, have already been applied in surgical scenarios with positive initial outcomes. Nonetheless, these methods have used manual or marker-based registration. In this work, we design an app...
Source: Journal of Digital Imaging - September 4, 2019 Category: Radiology Source Type: research

Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research
This paper had published originally without open access, but has since been republished with open access. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - September 4, 2019 Category: Radiology Source Type: research

Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification
In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author ’s institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The dec ision tree model, with an F1 score...
Source: Journal of Digital Imaging - September 3, 2019 Category: Radiology Source Type: research

Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network
AbstractWhole-heart coronary magnetic resonance angiography (WHCMRA) permits the noninvasive assessment of coronary artery disease without radiation exposure. However, the image resolution of WHCMRA is limited. Recently, convolutional neural networks (CNNs) have obtained increased interest as a method for improving the resolution of medical images. The purpose of this study is to improve the resolution of WHCMRA images using a CNN. Free-breathing WHCMRA images with 512  × 512 pixels (pixel size = 0.65 mm) were acquired in 80 patients with known or suspected coronary artery disease usi...
Source: Journal of Digital Imaging - August 26, 2019 Category: Radiology Source Type: research

Determining Follow-Up Imaging Study Using Radiology Reports
The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology rep...
Source: Journal of Digital Imaging - August 26, 2019 Category: Radiology Source Type: research

Evaluation of Audiovisual Reports to Enhance Traditional Emergency Musculoskeletal Radiology Reports
AbstractTraditional radiology reports are narrative texts that include a description of imaging findings. Recent implementation of advanced reporting software allows for incorporation of annotated key images and hyperlinks directly into text reports, but these tools usually do not substitute in-person consultations with radiologists, especially in challenging cases. Use of on-demand audio/visual reports with screen capture software is an emerging technology, providing a more engaged imaging service. Our study evaluates a video reporting tool that utilizes PACS integrated screen capture software for musculoskeletal imaging ...
Source: Journal of Digital Imaging - August 20, 2019 Category: Radiology Source Type: research

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters
AbstractPreoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection ...
Source: Journal of Digital Imaging - August 19, 2019 Category: Radiology Source Type: research

DynaMed Plus App Review
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - August 19, 2019 Category: Radiology Source Type: research

Can a Machine Learn from Radiologists ’ Visual Search Behaviour and Their Interpretation of Mammograms—a Deep-Learning Study
AbstractVisual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists ’ attentional level and the interpretation of mammograms. We seek to determine whether these models are practical and feasible for use in training and teaching programmes. Eight radiologists of varying experience levels in reading mammograms reviewed 120 two-view digital mammography cases (59 canc ers). Their search behaviour and decisions were captured using a head-mounted eye-tracking device and software allowin...
Source: Journal of Digital Imaging - August 13, 2019 Category: Radiology Source Type: research

Building Three-Dimensional Intracranial Aneurysm Models from 3D-TOF MRA: a Validation Study
AbstractTo create realistic three-dimensional (3D) vascular models from 3D time-of-flight magnetic resonance angiography (3D-TOF MRA) of an intracranial aneurysm (IA). Thirty-two IAs in 31 patients were printed using 3D-TOF MRA source images from polylactic acid (PLA) raw material. Two observers measured the maximum IA diameter at the longest width twice separately. A total mean of four measurements as well as each observer ’s individual average MRA lengths were calculated. After printing, 3D-printed anatomic models (PAM) underwent computed tomography (CT) acquisition and each observer measured them using the same al...
Source: Journal of Digital Imaging - August 13, 2019 Category: Radiology Source Type: research

Lung Segmentation on HRCT and Volumetric CT for Diffuse Interstitial Lung Disease Using Deep Convolutional Neural Networks
AbstractA robust lung segmentation method using a deep convolutional neural network (CNN) was developed and evaluated on high-resolution computed tomography (HRCT) and volumetric CT of various types of diffuse interstitial lung disease (DILD). Chest CT images of 617 patients with various types of DILD, including cryptogenic organizing pneumonia (COP), usual interstitial pneumonia (UIP), and nonspecific interstitial pneumonia (NSIP), were scanned using HRCT (1 –2-mm slices, 5–10-mm intervals) and volumetric CT (sub-millimeter thickness without intervals). Each scan was segmented using a conventional image proces...
Source: Journal of Digital Imaging - August 8, 2019 Category: Radiology Source Type: research

Accuracy and Reproducibility of Linear and Angular Measurements in Virtual Reality: a Validation Study
AbstractThe purpose of this experimental study is to validate linear and angular measurements acquired in a virtual reality (VR) environment via a comparison with the physical measurements. The hypotheses tested are as follows: VR linear and angular measurements (1) are equivalent to the corresponding physical measurements and (2) achieve a high degree of reproducibility. Both virtual and physical measurements were performed by two raters in four different sessions. A total of 40 linear and 15 angular measurements were acquired from three physical objects (an L-block, a hand model, and a dry skull) via the use of fiducial ...
Source: Journal of Digital Imaging - August 8, 2019 Category: Radiology Source Type: research

Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies
AbstractA second opinion about cancer stage is crucial when clinicians assess patient treatment progress. Staging is a process that takes into account description, location, characteristics, and possible metastasis of tumors in a patient. It should follow standards, such as the TNM Classification of Malignant Tumors. However, in clinical practice, the implementation of this process can be tedious and error prone. In order to alleviate these problems, we intend to assist radiologists by providing a second opinion in the evaluation of cancer stage. For doing this, we developed a TNM classifier based on semantic annotations, ...
Source: Journal of Digital Imaging - August 8, 2019 Category: Radiology Source Type: research

Assessing the Bone Age of Children in an Automatic Manner Newborn to 18 Years Range
The objective of this study is to assess the bone age of children from newborn to 18  years old in an automatic manner through computer vision methods including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale invariant feature transform (SIFT). Here, 442 left-hand radiographs are applied from the University of Southern California (USC) hand atlas. In th is experiment, for the first time, HOG–LBP–dense SIFT features with background subtraction are applied to assess the bone age of the subject group. For this purpose, features are extracted from the carpal and epiphyseal regions ...
Source: Journal of Digital Imaging - August 6, 2019 Category: Radiology Source Type: research

A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms
This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG) –based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.7...
Source: Journal of Digital Imaging - August 6, 2019 Category: Radiology Source Type: research

Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data
We describe a new PET viewing software known asImager-4D that provides a facile means of viewing and analyzing dynamic PET data and obtaining associated quantitative metrics including radiomic parameters. TheImager-4D was programmed in the Java language utilizing the FX extensions. It is executable on any system for which a Java w/FX compliant virtual machine is available. The software incorporates the ability to view and analyze dynamic data acquired with different types of dynamic protocols. For image display, the program maintains a built-in library of 62 different lookup tables with monochromatic and full-color distrib...
Source: Journal of Digital Imaging - August 6, 2019 Category: Radiology Source Type: research

What Can Pinterest Do for Radiology?
AbstractThe rapid growth of social media over the last decade soon convinced businesses including medical practices and academic medical centers to enter the social media fray —for profit, education, and expanding access. Launched in 2010, Pinterest (San Francisco, CA, USA) differed from many of the established social media platforms by presenting collection and curation features based on the sharing of images rather than text. Thus, Pinterest allows users to categorize website links using photos, GIFs, and videos, and catalog them for future consideration, saved on a virtual folder or “pinboard.” Faceboo...
Source: Journal of Digital Imaging - July 31, 2019 Category: Radiology Source Type: research

Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation
AbstractRetinal blood vessel extraction is considered to be the indispensable action for the diagnostic purpose of many retinal diseases. In this work, a parallel fully convolved neural network –based architecture is proposed for the retinal blood vessel segmentation. Also, the network performance improvement is studied by applying different levels of preprocessed images. The proposed method is experimented on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Ana lysis of the Retina) which are the widely accepted public database for this research area. The proposed work attains high accuracy,...
Source: Journal of Digital Imaging - July 24, 2019 Category: Radiology Source Type: research

Large-Scale Assessment of Scan-Time Variability and Multiple-Procedure Efficiency for Cross-Sectional Neuroradiological Exams in Clinical Practice
AbstractScheduling of CT and MR exams requires reasonable estimates for expected scan duration. However, scan-time variability and efficiency gains from combining multiple exams are not quantitatively well characterized. In this work, we developed an informatics approach to quantify typical duration, duration variability, and multiple-procedure efficiency on a large scale, and used the approach to analyze 48,766 CT- and MR-based neuroradiological exams performed over one year. We found MR exam durations demonstrated higher absolute variability, but lower relative variability and lower multiple-procedure efficiency, compare...
Source: Journal of Digital Imaging - July 10, 2019 Category: Radiology Source Type: research

Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations
AbstractIn order to successfully share patient data across multiple systems, a reliable method of linking patient records across disparate organizations is required. In Canada, within the province of Ontario, there are four centralized diagnostic imaging repositories (DIRs) that allow multiple hospitals and independent health facilities (IHF) to send diagnostic images and reports for the purpose of sharing patient data across the region (Nagels et al. J Digit Imaging 28: 188,2015). In 2017, the opportunity to consolidate the two regional DIRs that share the south-central and southeast area of the province was reviewed. The...
Source: Journal of Digital Imaging - July 10, 2019 Category: Radiology Source Type: research

Unlocking Radiology Reporting Data: an Implementation of Synoptic Radiology Reporting in Low-Dose CT Cancer Screening
AbstractCancer Care Ontario (CCO) is the clinical advisor to the Ontario Ministry of Health and Long-Term Care for the funding and delivery of cancer services. Data contained in radiology reports are inaccessible for analysis without significant manual cost and effort. Synoptic reporting includes highly structured reporting and discrete data capture, which could unlock these data for clinical and evaluative purposes. To assess the feasibility of implementing synoptic radiology reporting, a trial implementation was conducted at one hospital within CCO ’s Lung Cancer Screening Pilot for People at High Risk. This projec...
Source: Journal of Digital Imaging - July 9, 2019 Category: Radiology Source Type: research

Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization
AbstractSpeckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasou...
Source: Journal of Digital Imaging - July 3, 2019 Category: Radiology Source Type: research

Fully Automated and Real-Time Volumetric Measurement of Infarct Core and Penumbra in Diffusion- and Perfusion-Weighted MRI of Patients with Hyper-Acute Stroke
In conclusion, we demonstrate a fully automated and real-time algorithm to segment the penumbra and the infarct core regions based on PWI and DWI. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - July 2, 2019 Category: Radiology Source Type: research

Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features
AbstractIn this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of averag...
Source: Journal of Digital Imaging - June 26, 2019 Category: Radiology Source Type: research

Automated Billing Code Retrieval from MRI Scanner Log Data
AbstractAlthough the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit fro...
Source: Journal of Digital Imaging - June 25, 2019 Category: Radiology Source Type: research

AI Is Bringing USB Back: Implementing a Beta Chest X-ray Neural Network
AbstractIn a day and age of rapid technological growth and advancement in digital technology, quantum computing, and decentralized cloud computing, it is difficult to get excited about USB sticks, those little dongles that store only a few gigabytes and commonly get lost in the bottom of your bag. Well, they are making a major comeback at our institution in Canada. That is right, when Stanford and MIT are making the next Facebook and autonomous vehicles, we are bringing USB back. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - June 25, 2019 Category: Radiology Source Type: research

Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning
This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different...
Source: Journal of Digital Imaging - June 24, 2019 Category: Radiology Source Type: research

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks
AbstractExposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedback has been shown to sustain quality improvement and can remind and encourage technologists to comply with these methods. Previously, for use cases such as this, cumbersome manual techniques were required for such feedback. Modern deep learning methods utilizing convolutional neural networks (CNNs) can be used to develop...
Source: Journal of Digital Imaging - June 20, 2019 Category: Radiology Source Type: research

Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm
AbstractRadiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can extract measurements and their core descriptors, such as temporality, anatomical entity, imaging observation, RadLex descriptors, series number, image number, and segment from a wide variety of radiology reports (MR, CT, and mammogram). We created a hybrid NLP pipeline that integrates rule-based feature extraction modules ...
Source: Journal of Digital Imaging - June 20, 2019 Category: Radiology Source Type: research

Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning
AbstractUnstructured and semi-structured radiology reports represent an underutilized trove of information for machine learning (ML)-based clinical informatics applications, including abnormality tracking systems, research cohort identification, point-of-care summarization, semi-automated report writing, and as a source of weak data labels for training image processing systems. Clinical ML systems must beinterpretable to ensure user trust. To create interpretable models applicable to all of these tasks, we can build general-purpose systems which extract all relevant human-level assertions or “facts” documented ...
Source: Journal of Digital Imaging - June 19, 2019 Category: Radiology Source Type: research

Towards an Information Infrastructure for Medical Image Sharing
AbstractInformation infrastructures involve the notion of a shared, open infrastructure, constituting a space where people, organizations, and technical components associate to develop an activity. The current infrastructure for medical image sharing, based on PACS/DICOM technologies, does not constitute an information infrastructure since it is limited in its ability to share in a scalable, comprehensive, and secure manner. This paper proposes the DICOMFlow, a decentralized, distributed infrastructure model that aims to foment the formation of an information infrastructure in order to share medical images and teleradiolog...
Source: Journal of Digital Imaging - June 13, 2019 Category: Radiology Source Type: research

Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease
AbstractTo understand potential orbital biomarkers generated from computed tomography (CT) imaging in patients with thyroid eye disease. This is a retrospective cohort study. From a database of an ongoing thyroid eye disease research study at our institution, we identified 85 subjects who had both clinical examination and laboratory records supporting the diagnosis of thyroid eye disease and concurrent imaging prior to any medical or surgical intervention. Patients were excluded if imaging quality or type was not amenable to segmentation. The images of 170 orbits were analyzed with the developed automated segmentation tool...
Source: Journal of Digital Imaging - June 13, 2019 Category: Radiology Source Type: research

Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks
AbstractMachine learning has several potential uses in medical imaging for semantic labeling of images to improve radiologist workflow and to triage studies for review. The purpose of this study was to (1) develop deep convolutional neural networks (DCNNs) for automated classification of 2D mammography views, determination of breast laterality, and assessment and of breast tissue density; and (2) compare the performance of DCNNs on these tasks of varying complexity to each other. We obtained 3034 2D-mammographic images from the Digital Database for Screening Mammography, annotated with mammographic view, image laterality, ...
Source: Journal of Digital Imaging - June 13, 2019 Category: Radiology Source Type: research

Could Blockchain Technology Empower Patients, Improve Education, and Boost Research in Radiology Departments? An Open Question for Future Applications
AbstractBlockchain can be considered as a digital database of cryptographically validated transactions stored as blocks of data. Copies of the database are distributed on a peer-to-peer network adhering to a consensus protocol for authentication of new blocks into the chain. While confined to financial applications in the past, this technology is quickly becoming a hot topic in healthcare and scientific research. Potential applications in radiology range from upgraded monitoring of training milestones achievement for residents to improved control of clinical imaging data and easier creation of secure shared databases. (Sou...
Source: Journal of Digital Imaging - June 13, 2019 Category: Radiology Source Type: research

10 Steps to Strategically Build and Implement your Enterprise Imaging System: HIMSS-SIIM Collaborative White Paper
We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patien...
Source: Journal of Digital Imaging - June 8, 2019 Category: Radiology Source Type: research

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT
In this study, we implement a modified coarse-to-fine deep learning approach —the Recurrent Saliency Transformation Network (RSTN) for pelvic hematoma volume segmentation. RSTN previously yielded excellent results in pancreas segmentation, where low contrast with adjacent structures, small target volume, variable location, and fine contours are also problematic. We have cu rated a unique single-institution corpus of 253 C/A/P admission trauma CT studies in patients with bleeding pelvic fractures with manually labeled pelvic hematomas. We hypothesized that RSTN would result in sufficiently high Dice similarity coeffic...
Source: Journal of Digital Imaging - June 7, 2019 Category: Radiology Source Type: research

Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm
AbstractMorphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cytological images of breast cancer. The method initially is preprocessing images using color deconvolution to highlight hematoxylin-stained objects (nuclei). Next, convolutional neural network is applied to perform semantic segmentation of preprocessed image. It finds nucle...
Source: Journal of Digital Imaging - June 3, 2019 Category: Radiology Source Type: research

Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets
In this study, a fully automated lung lobe segmentation method with 3D U-Net was developed and validated with internal and external datasets. The volumetric chest CT scans of 196 normal and mild-to-moderate COPD patients from three centers were obtained. Each scan was segmented using a conventional image processing method and manually corrected by an expert thoracic radiologist to create gold standards. The lobe regions in the CT images were then segmented using a 3D U-Net architecture with a deep convolutional neural network (CNN) using separate training, validation, and test datasets. In addition, 40 independent external...
Source: Journal of Digital Imaging - May 31, 2019 Category: Radiology Source Type: research

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
AbstractDeep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - May 29, 2019 Category: Radiology Source Type: research

Artery/Vein Vessel Tree Identification in Near-Infrared Reflectance Retinographies
AbstractAn accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corres...
Source: Journal of Digital Imaging - May 29, 2019 Category: Radiology Source Type: research

Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors
AbstractThe increase of diabetic retinopathy patients and diabetic mellitus worldwide yields lot of challenges to ophthalmologists in the screening of diabetic retinopathy. Different signs of diabetic retinopathy were identified in retinal images taken through fundus photography. Among these stages, the early stage of diabetic retinopathy termed as microaneurysms plays a vital role in diabetic retinopathy patients. To assist the ophthalmologists, and to avoid vision loss among diabetic retinopathy patients, a computer-aided diagnosis is essential that can be used as a second opinion while screening diabetic retinopathy. On...
Source: Journal of Digital Imaging - May 29, 2019 Category: Radiology Source Type: research

Lexicomp App Review
AbstractLexicomp is a subscription-based pharmacological database app that encompasses everything you might need to know about medications. The app requires you to have a subscription service to be able to access the 20 pharmacological databases it has. There are three subscription plans, and each offers a different number of databases you can access. Having access to all databases will give the healthcare professional all —if not more—the information on a medication. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - May 28, 2019 Category: Radiology Source Type: research

Investigation of Low-Dose CT Lung Cancer Screening Scan “Over-Range” Issue Using Machine Learning Methods
In this study, we investigated an important factor affecting the CT dose—the scan length, for this CT exam. A neural network model based on the “UNET” framework was established to segment the lung region in the CT scout images. It was trained initially with 247 chest X-ray images and then with 40 CT scout images. The mean Intersection over Union (IOU) and Dice coefficient were reported to be 0.954 and 0.976, respectively. Lung scan boundaries were determined from this segmentation and compared with the boundaries marked by an expert for 150 validation images, resulti ng an average 4.7% difference. Seven h...
Source: Journal of Digital Imaging - May 17, 2019 Category: Radiology Source Type: research

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method
AbstractIn this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1  min after the injection; phase 3, 5 min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a com...
Source: Journal of Digital Imaging - May 16, 2019 Category: Radiology Source Type: research

RIL-Contour : a Medical Imaging Dataset Annotation Tool for and with Deep Learning
AbstractDeep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developedRIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly ann...
Source: Journal of Digital Imaging - May 14, 2019 Category: Radiology Source Type: research

Variabilities in Reference Standard by Radiologists and Performance Assessment in Detection of Pulmonary Embolism in CT Pulmonary Angiography
This study evaluated the variability of the radiologist-i dentified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markin gs from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variabil...
Source: Journal of Digital Imaging - May 9, 2019 Category: Radiology Source Type: research

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning
AbstractAssess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were ...
Source: Journal of Digital Imaging - May 9, 2019 Category: Radiology Source Type: research

Contrast-Enhancing Snapshot Narrow-Band Imaging Method for Real-Time Computer-Aided Cervical Cancer Screening
In this study, we aimed to utilize this difference to enhance the contrast between healthy and diseased tissues via snapshot narrow-band imaging (SNBI). Four narrow-band images centered at wavelengths of characteristic absorption/reflection peaks of hemoglobin were captured with zero-time delay in between by a custom-designed SNBI video camera. Then these spectral images were fused in real time into a single combined image to enhance the contrast between normal and abnormal tissues. Finally, a Euclidean distance algorithm was employed to classify the tissue into clinical meaningful tissue types. Two pre-clinical experiment...
Source: Journal of Digital Imaging - May 8, 2019 Category: Radiology Source Type: research

Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs
AbstractDevelop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded properly or inferred using other elements. Curation confirmed categorization and identified inaccurate labels due to human error. Augmentation enriched training data to semi-equilibrate classes. Classification and object detection models were developed on a dedicated workstation and tested on novel images. Receiver operating characteristic (ROC) curves, sensitivity, sp...
Source: Journal of Digital Imaging - May 7, 2019 Category: Radiology Source Type: research

Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier
AbstractTo assess whether application of a support vector machine learning algorithm to ancillary data obtained from posterior-anterior dual-energy X-ray absorptiometry (DEXA) studies could identify patients with lumbar spine (L1 –L4) vertebral body fractures without additional DEXA imaging or radiation. Three hundred seven patients (199 without any fractures of the spine, and 108 patients with at least one fracture of the L1, L2, L3, or L4 vertebral bodies) who had DEXA studies were evaluated. Ancillary data from DEXA out put was analyzed. The dataset was split into training (80%) and test (20%) datasets. Support ve...
Source: Journal of Digital Imaging - May 6, 2019 Category: Radiology Source Type: research