Robustifying Deep Networks for Medical Image Segmentation
AbstractThe purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. Two representative UNets were implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edem...
Source: Journal of Digital Imaging - September 20, 2021 Category: Radiology Source Type: research

Machine Learning in the Differentiation of Soft Tissue Neoplasms: Comparison of Fat-Suppressed T2WI and Apparent Diffusion Coefficient (ADC) Features-Based Models
This article aims to explore the feasibility of the whole tumor fat-suppressed (FS) T2WI and ADC features-based least absolute shrinkage and selection operator (LASSO)-logistic predictive models in the differentiation of soft tissue neoplasms (STN). The clinical and MR findings of 160 cases with 161 histologically proven STN were reviewed, retrospectively, 75 with diffusion-weighted imaging (DWI withb values of 50, 400, and 800  s/mm2). They were divided into benign and malignant groups and further divided into training (70%) and validation (30%) cohorts. The MR FS T2WI and ADC features-based LASSO-logistic models wer...
Source: Journal of Digital Imaging - September 20, 2021 Category: Radiology Source Type: research

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis
In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)text...
Source: Journal of Digital Imaging - September 20, 2021 Category: Radiology Source Type: research

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography
The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjace...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging
AbstractSeveral noise sources, such as the Johnson –Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (N LMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 imag...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network
AbstractMaximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This technology can better show organs, such as blood vessels, arteries, veins, and bronchi and so forth, from different directions, which could bring more intuitive and comprehensive results for doctors in the diagnosis of related diseases. However, in this traditional projection technology, the details of the small projected target are not clearly visualized when the proj...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Quantitative Three-Dimensional Assessment of the Pharmacokinetic Parameters of Intra- and Peri-tumoural Tissues on Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging
AbstractWe aimed to assess the feasibility of three-dimensional (3D) segmentation and to investigate whether semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are associated with traditional prognostic factors for breast cancer. In addition, we evaluated whether both intra-tumoural and peri-tumoural DCE parameters can differentiate the breast cancers that are more aggressive from those that are less aggressive. Consecutive patients with newly diagnosed invasive breast cancer and structural breast MRI (3.0  T) were included after informed consent. Fifty-six patients (mean age, ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Effects of the Nasal Cavity Complexity on the Pharyngeal Airway Fluid Mechanics: A Computational Study
AbstractThe impact of the human nasal airway complexity on the pharyngeal airway fluid mechanics is investigated at inspiration. It is the aim to find a suitable degree of geometrical reduction that allows for an efficient segmentation of the human airways from cone-beam computed tomography images. The flow physics is simulated by a lattice Boltzmann method on high-performance computers. For two patients, the flow field through the complete upper airway is compared to results obtained from three surface variants with continuously decreasing complexity. The most complex reduced airway model includes the middle and inferior ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Heterogeneous Stitching of X-ray Images According to Homographic Evaluation
AbstractThe C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

3D Isotropic Super-resolution Prostate MRI Using Generative Adversarial Networks and Unpaired Multiplane Slices
AbstractWe developed a deep learning –based super-resolution model for prostate MRI. 2D T2-weighted turbo spin echo (T2w-TSE) images are the core anatomical sequences in a multiparametric MRI (mpMRI) protocol. These images have coarse through-plane resolution, are non-isotropic, and have long acquisition times (approximately 10–15  min). The model we developed aims to preserve high-frequency details that are normally lost after 3D reconstruction. We propose a novel framework for generating isotropic volumes using generative adversarial networks (GAN) from anisotropic 2D T2w-TSE and single-shot fast spin ec...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network
AbstractMaximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This technology can better show organs, such as blood vessels, arteries, veins, and bronchi and so forth, from different directions, which could bring more intuitive and comprehensive results for doctors in the diagnosis of related diseases. However, in this traditional projection technology, the details of the small projected target are not clearly visualized when the proj...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Quantitative Three-Dimensional Assessment of the Pharmacokinetic Parameters of Intra- and Peri-tumoural Tissues on Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging
AbstractWe aimed to assess the feasibility of three-dimensional (3D) segmentation and to investigate whether semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are associated with traditional prognostic factors for breast cancer. In addition, we evaluated whether both intra-tumoural and peri-tumoural DCE parameters can differentiate the breast cancers that are more aggressive from those that are less aggressive. Consecutive patients with newly diagnosed invasive breast cancer and structural breast MRI (3.0  T) were included after informed consent. Fifty-six patients (mean age, ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography
The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjace...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging
AbstractSeveral noise sources, such as the Johnson –Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (N LMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 imag...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Effects of the Nasal Cavity Complexity on the Pharyngeal Airway Fluid Mechanics: A Computational Study
AbstractThe impact of the human nasal airway complexity on the pharyngeal airway fluid mechanics is investigated at inspiration. It is the aim to find a suitable degree of geometrical reduction that allows for an efficient segmentation of the human airways from cone-beam computed tomography images. The flow physics is simulated by a lattice Boltzmann method on high-performance computers. For two patients, the flow field through the complete upper airway is compared to results obtained from three surface variants with continuously decreasing complexity. The most complex reduced airway model includes the middle and inferior ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

Heterogeneous Stitching of X-ray Images According to Homographic Evaluation
AbstractThe C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is ...
Source: Journal of Digital Imaging - September 10, 2021 Category: Radiology Source Type: research

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
AbstractReal-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals ’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georg...
Source: Journal of Digital Imaging - August 17, 2021 Category: Radiology Source Type: research

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors
This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the who...
Source: Journal of Digital Imaging - August 17, 2021 Category: Radiology Source Type: research

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
AbstractReal-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals ’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georg...
Source: Journal of Digital Imaging - August 17, 2021 Category: Radiology Source Type: research

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors
This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the who...
Source: Journal of Digital Imaging - August 17, 2021 Category: Radiology Source Type: research

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information
AbstractThe aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients ’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiom ic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for ...
Source: Journal of Digital Imaging - August 11, 2021 Category: Radiology Source Type: research

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray
This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional neural network (CNN) with plain X-ray and to validate its use across hospitals through internal and external validation sets. This is a retrospective study using hip and pelvic anteroposterior films for training and detecting femoral neck fracture through residual neural network (ResNet) 18 with convolutional block attention module (CBAM)  +  + . The study was performed at two tertiary hospitals between February and May 2020 and used data from Janu...
Source: Journal of Digital Imaging - August 11, 2021 Category: Radiology Source Type: research

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information
AbstractThe aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients ’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiom ic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for ...
Source: Journal of Digital Imaging - August 11, 2021 Category: Radiology Source Type: research

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray
This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional neural network (CNN) with plain X-ray and to validate its use across hospitals through internal and external validation sets. This is a retrospective study using hip and pelvic anteroposterior films for training and detecting femoral neck fracture through residual neural network (ResNet) 18 with convolutional block attention module (CBAM)  +  + . The study was performed at two tertiary hospitals between February and May 2020 and used data from Janu...
Source: Journal of Digital Imaging - August 11, 2021 Category: Radiology Source Type: research

Visible Body Human Anatomy Atlas: Innovative Anatomy Learning
AbstractVisible Body Human Anatomy Atlas is a subscription-based learning tool for health science students and clinicians to build and strengthen knowledge of human anatomy. This app contains thousands of 3D models of gross anatomy and microanatomy, cadaver lab simulations, comparisons to diagnostic imaging, quizzes, and patient education videos. Here we explore the app ’s strengths and weaknesses through discussion of its features and usability. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - August 2, 2021 Category: Radiology Source Type: research

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study
AbstractHere, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning ...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

RadSimPE — a Radiology Workstation Simulator for Perceptual Education
AbstractRecent studies have demonstrated the effectiveness of simulation in radiology perceptual education. While current software exists for perceptual research, these software packages are not optimized for inclusion of educational materials and do not have full integration for presentation of educational materials. To address this need, we created a user-friendly software application,RadSimPE.RadSimPE simulates a radiology workstation, displays radiology cases for quantitative assessment, and incorporates educational materials in one seamless software package. RadSimPE provides simple customizability for a variety of ed...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data
AbstractThe development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. For this task, fully convolutional network (FCN)-based architectures are used to build end-to-end segme...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study
This study aimed to validate a software-based method for automated segmentation and measurement of wounds on photographic images using the Mask R-CNN (Region-based Convolutional Neural Network). During the validation, five medical experts manually segmented an independent dataset with 35 wound photographs at two different points in time with an interval of 1  month. Simultaneously, the dataset was automatically segmented using the Mask R-CNN. Afterwards, the segmentation results were compared, and intra- and inter-rater analyses performed. In the statistical evaluation, an analysis of variance (ANOVA) was carried out ...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees
AbstractArtificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

On the Use of Virtual Reality for Medical Imaging Visualization
AbstractAdvanced visualization of medical imaging has been a motive for research due to its value for disease analysis, surgical planning, and academical training. More recently, attention has been turning toward mixed reality as a means to deliver more interactive and realistic medical experiences. However, there are still many limitations to the use of virtual reality for specific scenarios. Our intent is to study the current usage of this technology and assess the potential of related development tools for clinical contexts. This paper focuses on virtual reality as an alternative to today ’s majority of slice-base...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Anatomic Point –Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs
In conclusion, a reliable a natomic point–based lung segmentation method for CXRs has been developed with expected utility for establishing reference standards for machine learning applications. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

RadSimPE — a Radiology Workstation Simulator for Perceptual Education
AbstractRecent studies have demonstrated the effectiveness of simulation in radiology perceptual education. While current software exists for perceptual research, these software packages are not optimized for inclusion of educational materials and do not have full integration for presentation of educational materials. To address this need, we created a user-friendly software application,RadSimPE.RadSimPE simulates a radiology workstation, displays radiology cases for quantitative assessment, and incorporates educational materials in one seamless software package. RadSimPE provides simple customizability for a variety of ed...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

On the Use of Virtual Reality for Medical Imaging Visualization
AbstractAdvanced visualization of medical imaging has been a motive for research due to its value for disease analysis, surgical planning, and academical training. More recently, attention has been turning toward mixed reality as a means to deliver more interactive and realistic medical experiences. However, there are still many limitations to the use of virtual reality for specific scenarios. Our intent is to study the current usage of this technology and assess the potential of related development tools for clinical contexts. This paper focuses on virtual reality as an alternative to today ’s majority of slice-base...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study
AbstractHere, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning ...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data
AbstractThe development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. For this task, fully convolutional network (FCN)-based architectures are used to build end-to-end segme...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study
This study aimed to validate a software-based method for automated segmentation and measurement of wounds on photographic images using the Mask R-CNN (Region-based Convolutional Neural Network). During the validation, five medical experts manually segmented an independent dataset with 35 wound photographs at two different points in time with an interval of 1  month. Simultaneously, the dataset was automatically segmented using the Mask R-CNN. Afterwards, the segmentation results were compared, and intra- and inter-rater analyses performed. In the statistical evaluation, an analysis of variance (ANOVA) was carried out ...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees
AbstractArtificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited...
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Anatomic Point –Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs
In conclusion, a reliable a natomic point–based lung segmentation method for CXRs has been developed with expected utility for establishing reference standards for machine learning applications. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - July 29, 2021 Category: Radiology Source Type: research

Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection
AbstractPatients who are intubated with endotracheal tubes often receive chest x-ray (CXR) imaging to determine whether the tube is correctly positioned. When these CXRs are interpreted by a radiologist, they evaluate whether the tube needs to be repositioned and typically provide a measurement in centimeters between the endotracheal tube tip and carina. In this project, a large dataset of endotracheal tube and carina bounding boxes was annotated on CXRs, and a machine-learning model was trained to generate these boxes on new CXRs and to calculate a distance measurement between the tube and carina. This model was applied t...
Source: Journal of Digital Imaging - July 28, 2021 Category: Radiology Source Type: research

Panoramic Dental Reconstruction for Faster Detection of Dental Pathology on Medical Non-dental CT Scans: a Proof of Concept from CT Neck Soft Tissue
AbstractEven though teeth are often included in the field of view for a variety of medical CT studies, dental pathology is often missed by radiologists. Given the myriad morbidity and occasional mortality associated with sequelae of dental pathology, an important goal is to decrease these false negatives. However, given the ever-increasing volume of cases studies that radiologists have to read and the number of structures and diseases they have to evaluate, it is important not to place undue time restraints on the radiologist to this end. We hypothesized that generating panoramic dental radiographs from non-dental CT scans...
Source: Journal of Digital Imaging - July 13, 2021 Category: Radiology Source Type: research

There ’s a New Sheriff in Town: When Enterprise IT Takes Over Imaging IT
AbstractThe consolidation of information technology (IT) teams from individual facilities to an enterprise-wide reporting structure and the transition of IT staff from operating within a diagnostic imaging department, such as Radiology, to an enterprise IT group is common. The plan to optimize this workforce can have undesirable and unintended consequences, if not done correctly. For those organizations seeking to optimize their workforce to deliver the best possible IT services, including to areas that produce and use medical imaging, this can be an exercise of balancing specialized knowledge and centralized staffing capa...
Source: Journal of Digital Imaging - July 13, 2021 Category: Radiology Source Type: research

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks
AbstractData augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we ...
Source: Journal of Digital Imaging - July 12, 2021 Category: Radiology Source Type: research

Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography
This study introduces a method based on artificial intelligence neural network to predict and detect either regular caries or RRC in HNC patients u nder RT using features extracted from panoramic radiograph. We selected fifteen HNC patients (13 men and 2 women) to analyze, retrospectively, their panoramic dental images, including 420 teeth. Two dentists manually labeled the teeth to separate healthy and teeth with either type caries. They also labeled the teeth by resistant and vulnerable, as predictive labels telling about RT aftermath caries. We extracted 105 statistical/morphological image features of the teeth using Py...
Source: Journal of Digital Imaging - July 12, 2021 Category: Radiology Source Type: research

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model
AbstractTo develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The g round...
Source: Journal of Digital Imaging - July 9, 2021 Category: Radiology Source Type: research

Artificial Intelligence –Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration
AbstractThe purpose of this study was to detect the presence of retinitis pigmentosa (RP) based on color fundus photographs using a deep learning model. A total of 1670 color fundus photographs from the Taiwan inherited retinal degeneration project and National Taiwan University Hospital were acquired and preprocessed. The fundus photographs were labeled RP or normal and divided into training and validation datasets (n = 1284) and a test dataset (n = 386). Three transfer learning models based on pre-trained Inception V3, Inception Resnet V2, and Xception deep learning architectures, respectively...
Source: Journal of Digital Imaging - July 9, 2021 Category: Radiology Source Type: research

DICOM in Dermoscopic Research: an Experience Report and a Way Forward
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - July 9, 2021 Category: Radiology Source Type: research