Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification
AbstractClassification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients ’ survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning–based diagnostic methods. To address this problem, we propose a multi- model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three ...
Source: Journal of Digital Imaging - June 30, 2020 Category: Radiology Source Type: research

Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet
AbstractMelanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtaine...
Source: Journal of Digital Imaging - June 30, 2020 Category: Radiology Source Type: research

Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study
This study suggests that pre-training strategy influences significant performance differences, especially in the case of distinguishing recalled- benign from malignant and benign screening patients. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - June 30, 2020 Category: Radiology Source Type: research

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network
AbstractThe accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power a...
Source: Journal of Digital Imaging - June 30, 2020 Category: Radiology Source Type: research

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation
AbstractRecent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. ...
Source: Journal of Digital Imaging - June 30, 2020 Category: Radiology Source Type: research

A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images
AbstractThe mass of the lower extremity muscles is a clinically significant metric. Manual segmentation of these muscles is a time-consuming task. Most of the segmentation methods for the thigh muscles are based on statistical models and atlases which need manually segmented datasets. The goal of this work is an automatic segmentation of the thigh muscles with only one initial segmented slice. A new automatic method is proposed for concurrent individual thigh muscles segmentation using a hybrid level set method and anatomical information of the muscles. In the proposed method, the muscle regions are extracted by the Fast a...
Source: Journal of Digital Imaging - June 25, 2020 Category: Radiology Source Type: research

Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy
In this study the feasibility of applying video coding algorithms for 4D-CBCT image compression was investigated. Prior to compression 4D-CBCT images were arranged in an order based on breathing phase or slice location for input sequence of video encoder. Median filtering was applied to suppress noise and artifact of 4D-CBCT for improved image quality. Three popular video coding algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG-4) were tested and their performances were evaluated on a publicly available 4D-CBCT database. The average compression ratio of MPEG-4 was 135, while the values of Motion JPEG AVI and Motion J...
Source: Journal of Digital Imaging - June 24, 2020 Category: Radiology Source Type: research

Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification
AbstractTo use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Ground truth labels of Garden fracture classification were applied as follows: (1) 127 Garden I and II fracture radiographs, (2) 610 Garden III and IV fracture radiographs, and (3) 326 normal hip radiographs. After localization by an initial network, a second CNN classified the images as Garden I/II fracture, Garden III/IV fracture, or no fracture. Adva...
Source: Journal of Digital Imaging - June 24, 2020 Category: Radiology Source Type: research

Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images
AbstractThe automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies ...
Source: Journal of Digital Imaging - June 19, 2020 Category: Radiology Source Type: research

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales
AbstractTo evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Aver...
Source: Journal of Digital Imaging - June 15, 2020 Category: Radiology Source Type: research

Simulating Tissues with 3D-Printed and Castable Materials
AbstractManufacturing technologies continue to be developed and utilized in medical prototyping, simulations, and imaging phantom production. For radiologic image-guided simulation and instruction, models should ideally have similar imaging characteristics and physical properties to the tissues they replicate. Due to the proliferation of different printing technologies and materials, there is a diverse and broad range of approaches and materials to consider before embarking on a project. Although many printed materials ’ biomechanical parameters have been reported, no manufacturer includes medical imaging properties ...
Source: Journal of Digital Imaging - June 15, 2020 Category: Radiology Source Type: research

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images
AbstractLung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used...
Source: Journal of Digital Imaging - June 15, 2020 Category: Radiology Source Type: research

Patient Access to an Online Portal for Outpatient Radiological Images and Reports: Two Years ’ Experience
AbstractTo assess the incidence of outpatient examinations delivered through a web portal in the Latium Region in 2  years and compare socio-demographic characteristics of these users compared to the total of examinations performed. All radiological exams (including MRI, X-ray and CT) performed from March 2017 to February 2019 were retrospectively analysed. For each exam, anonymized data of users who attended th e exam were extracted and their characteristics were compared according to digital access to the reports. Overall, 9068 exams were performed in 6720 patients (55.8% males, median age 58 years, interquarti...
Source: Journal of Digital Imaging - June 9, 2020 Category: Radiology Source Type: research

Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer
This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corr...
Source: Journal of Digital Imaging - June 9, 2020 Category: Radiology Source Type: research

Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics
In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver MRI scans for 99 patients who were diagnosed with HCC. These patients received surgery and pathology-confirmed diagnosis of mVI. We extracted radiomics features from manually segmented HCC regions and built machine learning classifiers to predict mVI status. We compared the performance of such classifiers when built on five MRI sequences used both individually and combined. We investigated the effects of using features extracted from the tumor region...
Source: Journal of Digital Imaging - June 3, 2020 Category: Radiology Source Type: research

A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports
AbstractThe aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 1410 BT-RADS structured reports dated from January 2014 to December 2017 and a test set of 109 unstructured brain MR reports dated from January 2010 to December 2014. Text vector representations and semantic word embeddings were generated from individual report sections (i.e., “History,” “Findings,” etc.) using Tf-idf statistics and a fine-tuned wo...
Source: Journal of Digital Imaging - June 3, 2020 Category: Radiology Source Type: research

Overcoming Challenges for Successful PACS Installation in Low-Resource Regions: Our Experience in Nigeria
AbstractIn this paper, we walk you through our challenges, successes, and experience while participating in a Global Health Outreach Project at the University College Hospital (UCH) Ibadan, Nigeria. The scope of the project was to install a Picture Archive and Communication System (PACS) to establish a centralized viewing network at UCH ’s Radiology Department, for each of their digital modalities. Installing a PACS requires robust servers, the ability to retrieve and archive studies, ensuring workstations can view studies, and the configuration of imaging modalities to send studies. We anticipated that we might expe...
Source: Journal of Digital Imaging - June 3, 2020 Category: Radiology Source Type: research

Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)
In this study, we designed and trained an R-CNN for detecting meniscus region in MRI data sequence. We used transfer learning for training R-CNN with a small amount of meniscus data. After detection of the meniscus region by R-CNN, we segmented meniscus by morphological image analysis using two different MRI sequences. Automatic detection of the meniscus region with R-CNN made the meniscus segmentation process easier, and the use of different contrast features of two different image sequences allowed us to differentiate the meniscus from its surroundings. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - June 2, 2020 Category: Radiology Source Type: research

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification
AbstractAccurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture...
Source: Journal of Digital Imaging - May 21, 2020 Category: Radiology Source Type: research

So You Want to Develop an App for Radiology Education? What You Need to Know to Be Successful
AbstractApple changed the communications landscape with its 2007 introduction of the iPhone. In little more than a decade, most Americans have become smartphone owners. With more than 200,000 applications (apps) for education available in the Apple App Store, we can infer that many smartphone owners use their devices to learn. Several surveys of medical students reveal that apps do indeed enhance clinical knowledge and provide comparable training with textbooks. We launched our first iOS app, the CTisus iQuiz, in 2010 in response to the growing number of portable devices, and with an intention to grow alongside technology....
Source: Journal of Digital Imaging - May 15, 2020 Category: Radiology Source Type: research

An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases
AbstractThe main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient ’s lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pat tern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)–ba...
Source: Journal of Digital Imaging - May 12, 2020 Category: Radiology Source Type: research

Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks
We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - May 6, 2020 Category: Radiology Source Type: research

Skin Lesion Segmentation with Improved Convolutional Neural Network
AbstractRecently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used...
Source: Journal of Digital Imaging - May 6, 2020 Category: Radiology Source Type: research

Is a Picture Really Worth More than a Thousand Words? Which Instagram Post Types Elicit the Best Response for Radiology Education
AbstractSince its 2010 launch, Instagram has rapidly risen to become one of the leading social media in the world, with more than one billion monthly users. Within the field of radiology, perhaps because of the image-weighted nature of the platform, several prominent organizations host Instagram accounts, including the Radiological Society of North America, American Roentgen Ray Society, American College of Radiology, and the American Board of Radiology. We started our own Instagram account in January 2016 because of the popularity of this social media platform among medical students. Our website contains over 260,000 imag...
Source: Journal of Digital Imaging - May 5, 2020 Category: Radiology Source Type: research

Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network
AbstractHistology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC) tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result radiologists or oncologists find it difficult to detect proper histology of NSCLC tumors. The non-invasive methods such as machine learning may play a useful role to predict NSCLC histology by using medical image biomarkers. Few attempts have so far been made to predict NSCLC histology by considering all the major subtypes. The present study aimed to develop a more accurate deep learning model by clubbing convolutional and bidirectional recur...
Source: Journal of Digital Imaging - April 24, 2020 Category: Radiology Source Type: research

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation
AbstractSince morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution ...
Source: Journal of Digital Imaging - April 22, 2020 Category: Radiology Source Type: research

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing
AbstractStructured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex ® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So...
Source: Journal of Digital Imaging - April 21, 2020 Category: Radiology Source Type: research

Using Facebook Live to Advocate Breast Cancer Screening
AbstractWith current conflicting and confusing screening mammography guidelines between major medical organizations, radiologists have an opportunity to educate and advocate for patients using the power of social media. The authors provide a brief overview on the impact of social media in radiology, in particular Facebook, as well as challenges encountered by radiologists as they establish an online presence, and how to effectively use Facebook Live to advocate for screening mammography. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - April 21, 2020 Category: Radiology Source Type: research

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study
In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending ...
Source: Journal of Digital Imaging - April 20, 2020 Category: Radiology Source Type: research

Unlocking the PACS DICOM Domain for its Use in Clinical Research Data Warehouses
AbstractClinical Data Warehouses (DWHs) are used to provide researchers with simplified access to pseudonymized and homogenized clinical routine data from multiple primary systems. Experience with the integration of imaging and metadata from picture archiving and communication systems (PACS), however, is rare. Our goal was therefore to analyze the viability of integrating a production PACS with a research DWH to enable DWH queries combining clinical and medical imaging metadata and to enable the DWH to display and download images ad hoc. We developed an application interface that enables to query the production PACS of a l...
Source: Journal of Digital Imaging - April 20, 2020 Category: Radiology Source Type: research

Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules
In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multi-a ttribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - April 13, 2020 Category: Radiology Source Type: research

Rapid Design and Development of a Network Isolated DICOM Service Class Provider Device
AbstractIntegrating vendor equipment and instruments into a corporate pharmaceutical research environment can be challenging and in light of recently reported cyber-attacks across industries and ongoing threats, additional security measures add to the challenge. In theory, Windows 10-based equipment coupled with the Digital Imaging and Communications in Medicine (DICOM) protocol should make it easier for instrument integration. A challenge arose with the onboarding of 2 new Microsoft Windows 10, DICOM compliant, Pre-clinical Positron Emission Tomography and Computed Tomography (PET/CT) instruments post acquisition when we ...
Source: Journal of Digital Imaging - April 8, 2020 Category: Radiology Source Type: research

Ontology-Based Radiology Teaching File Summarization, Coverage, and Integration
AbstractRadiology teaching file repositories contain a large amount of information about patient health and radiologist interpretation of medical findings. Although valuable for radiology education, the use of teaching file repositories has been hindered by the ability to perform advanced searches on these repositories given the unstructured format of the data and the sparseness of the different repositories. Our term coverage analysis of two major medical ontologies, Radiology Lexicon (RadLex) and Unified Medical Language System (UMLS) Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and two teaching file...
Source: Journal of Digital Imaging - April 6, 2020 Category: Radiology Source Type: research

Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset
AbstractPneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a lar...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Using 3D-Printed Mesh-Like Brain Cortex with Deep Structures for Planning Intracranial EEG Electrode Placement
We present a novel method of 3D printing a brain that allows for the simulation of placement of all types of intracranial electrodes. We used a DICOM dataset of a T1-weighted 3D-FSPGR brain MRI from one subj ect. The segmentation tools ofMaterialise Mimics 21.0 were used to remove the osseous anatomy from brain parenchyma.Materialise 3-matic 13.0 was then utilized in order to transform the cortex of the segmented brain parenchyma into a mesh-like surface. Using3-matic tools, the model was modified to incorporate deep brain structures and create an opening in the medial aspect. The final model was then 3D printed as a cereb...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Automating Import and Reconciliation of Outside Examinations Submitted to an Academic Radiology Department
AbstractAlthough advances in electronic image sharing have made continuity of patient care easier, currently, the majority of outside studies are received on CD. At our institution, there were 9 full-time employees (FTE) at three locations using three workflows to manually upload, schedule, and process studies to PACS. As the demand to view and store outside studies has grown, so has the processing turnaround time. To reduce turnaround time and the need for human intervention, we developed an automated workflow to import outside studies from a CD to our PACS and reconcile them with an internal accession number and exam cod...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Assessment of Dynamic Change of Coronary Artery Geometry and Its Relationship to Coronary Artery Disease, Based on Coronary CT Angiography
AbstractTo investigate the relationship between dynamic changes of coronary artery geometry and coronary artery disease (CAD) using computed tomography (CT). Seventy-one patients underwent coronary CT angiography with retrospective electrocardiographic gating. End-systolic (ES) and end-diastolic (ED) phases were automatically determined by dedicated software. Centerlines were extracted for the right and left coronary artery. Differences between ES and ED curvature and tortuosity were determined. Associations of change in geometrical parameters with plaque types and degree of stenosis were investigated using linear mixed mo...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver CT
AbstractA medical annotation system for radiology images extracts clinically useful information from the images, allowing the machines to infer useful abstract semantics and become capable of automatic reasoning and making diagnostic decision. It also supplies human-interpretable explanation for the images. We have implemented a computerized framework that, given a liver CT image, predicts radiological annotations with high accuracy, in order to generate a structured report, which includes predicting very specific high-level semantic content. Each report of a liver CT image is related to different inhomogeneous parts like ...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Viewing Imaging Studies: How Patient Location and Imaging Site Affect Referring Physicians
AbstractThe purpose of this study was to assess if clinical indications, patient location, and imaging sites predict the viewing pattern of referring physicians for CT and MR of the head, chest, and abdomen. Our study included 166,953 CT/MR images of head/chest/abdomen in 2016 –2017 in the outpatient (OP,n = 83,981 CT/MR), inpatient (IP,n = 51,052), and emergency (ED,n = 31,920) settings. There were 125,329 CT/MR performed in the hospital setting and 41,624 in one of the nine off-campus locations. We extracted information regarding body region (head/chest/abdomen), patient lo...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer
AbstractLow-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced...
Source: Journal of Digital Imaging - April 1, 2020 Category: Radiology Source Type: research

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning
AbstractThe diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of...
Source: Journal of Digital Imaging - March 23, 2020 Category: Radiology Source Type: research

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach
AbstractIn developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. I...
Source: Journal of Digital Imaging - March 19, 2020 Category: Radiology Source Type: research

Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning
This study explores an automatic diagnosis method to predict unnecessary nodule biopsy from a small, unbalanced, and pathologically proven database. The automatic diagnosis method is based on a convolutional neural network (CNN) model. Because of the small and unbalanced samples, the presented method aims to improve the transfer learning capability via the VGG16 architecture and optimize the related transfer learning parameters. For comparison purpose, a traditional machine learning method is implemented, which extracts the texture features and classifies the features by support vector machine (SVM). The database includes ...
Source: Journal of Digital Imaging - March 6, 2020 Category: Radiology Source Type: research

An Embedded Multi-branch 3D Convolution Neural Network for False Positive Reduction in Lung Nodule Detection
The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates. Facing the challenge of the classification task, we propose a novel 3D convolution neural network (CNN) to reduce false positives in lung nodule detection. The novel 3D CNN includes embedded multiple branches in its structure. Each branch processes a feature map from a layer with different depths. All of these branches are cascaded at their ends; thus, features from different depth layers are combined to predict the categories of candidates. The proposed method obtains a competitive score in lung nodule candidate ...
Source: Journal of Digital Imaging - February 24, 2020 Category: Radiology Source Type: research

Surface Point Cloud Ultrasound with Transcranial Doppler: Coregistration of Surface Point Cloud Ultrasound with Magnetic Resonance Angiography for Improved Reproducibility, Visualization, and Navigation in Transcranial Doppler Ultrasound
AbstractTranscranial Doppler (TCD) ultrasound is a standard tool used in the setting of recent sub-arachnoid hemorrhage (SAH). By tracking velocity in the circle-of-Willis vessels, vasospasm can be detected as interval velocity increase. For this disease process, repeated TCD velocity measurements over many days is the basis for its usefulness. However, a key limitation to TCD is its user dependence, which is itself largely due to the fact that exact information about probe positioning is lost between subsequent scans. Surface point cloud ultrasound (SPC-US) was recently introduced as a general approach combining ultrasoun...
Source: Journal of Digital Imaging - February 19, 2020 Category: Radiology Source Type: research

Natural Language Processing in Dutch Free  Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology
AbstractReports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free ...
Source: Journal of Digital Imaging - February 19, 2020 Category: Radiology Source Type: research

(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - February 19, 2020 Category: Radiology Source Type: research

A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images
AbstractThe purpose of this research is to exploit a weak and semi-supervised deep learning framework to segment prostate cancer in TRUS images, alleviating the time-consuming work of radiologists to draw the boundary of the lesions and training the neural network on the data that do not have complete annotations. A histologic-proven benchmarking dataset of 102 case images was built and 22 images were randomly selected for evaluation. Some portion of the training images were strong supervised, annotated pixel by pixel. Using the strong supervised images, a deep learning neural network was trained. The rest of the training ...
Source: Journal of Digital Imaging - February 10, 2020 Category: Radiology Source Type: research

Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades
AbstractThe grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012 –2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including...
Source: Journal of Digital Imaging - February 10, 2020 Category: Radiology Source Type: research

Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network
AbstractIn the diagnosis of neurodegenerative disorders, F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used for its ability to detect functional changes at early stages of disease process. However, anatomical information from another modality (CT or MRI) is still needed to properly interpret and localize the radiotracer uptake due to its low spatial resolution. Lack of structural information limits segmentation and accurate quantification of the18F-FDG PET/CT. The correct segmentation of the brain compartment in18F-FDG PET/CT will enable the quantitative analysis of the18F-FDG...
Source: Journal of Digital Imaging - February 10, 2020 Category: Radiology Source Type: research