Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification
AbstractThis work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution. The correlation between two brain regions is measured by the Jensen-Shannon divergence that describes the statisti...
Source: Journal of Digital Imaging - October 1, 2023 Category: Radiology Source Type: research
An AI-Based Image Quality Control Framework for Knee Radiographs
In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients co...
Source: Journal of Digital Imaging - October 1, 2023 Category: Radiology Source Type: research
What Matters in Radiological Image Segmentation? Effect of Segmentation Errors on the Diagnostic Related Features
AbstractSegmentation is a crucial step in extracting the medical image features for clinical diagnosis. Though multiple metrics have been proposed to evaluate the segmentation performance, there is no clear study on how or to what extent the segmentation errors will affect the diagnostic related features used in clinical practice. Therefore, we proposed a segmentation robustness plot (SRP) to build the link between segmentation errors and clinical acceptance, where relative area under the curve (R-AUC) was designed to help clinicians to identify the robust diagnostic related image features. In experiments, we first selecte...
Source: Journal of Digital Imaging - October 1, 2023 Category: Radiology Source Type: research
Correction to: Lossy Image Compression in a Preclinical Multimodal Imaging Study
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - October 1, 2023 Category: Radiology Source Type: research
Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment
AbstractGlucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clinical practice. However, visual assessment of GLUT-1 scores is subjective and consequently prone to inter-pathologist variability. Therefore, this study proposes an automated method for assessing GLUT-1 scores in IHC colorectal carcinoma images. For this purpose, we leverage deep transfer learning methodologies for evaluating the performance of six different pre-trained c...
Source: Journal of Digital Imaging - September 5, 2023 Category: Radiology Source Type: research
Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis —a Pilot Study
AbstractThe purpose of this study is to evaluate the accuracy and inter-observer agreement of a quantitative pulmonary surface irregularity (PSI) score on high-resolution chest CT (HRCT) for predicting transplant-free survival in patients with IPF. For this IRB-approved HIPAA-compliant retrospective single-center study, adult patients with IPF and HRCT imaging (N = 50) and an age- and gender-matched negative control group with normal HRCT imaging (N = 50) were identified. Four independent readers measured the PSI score in the midlungs on HRCT images using dedicated software while blinded to clinical data. At-test w...
Source: Journal of Digital Imaging - September 5, 2023 Category: Radiology Source Type: research
Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment
AbstractGlucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clinical practice. However, visual assessment of GLUT-1 scores is subjective and consequently prone to inter-pathologist variability. Therefore, this study proposes an automated method for assessing GLUT-1 scores in IHC colorectal carcinoma images. For this purpose, we leverage deep transfer learning methodologies for evaluating the performance of six different pre-trained c...
Source: Journal of Digital Imaging - September 5, 2023 Category: Radiology Source Type: research
Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis —a Pilot Study
AbstractThe purpose of this study is to evaluate the accuracy and inter-observer agreement of a quantitative pulmonary surface irregularity (PSI) score on high-resolution chest CT (HRCT) for predicting transplant-free survival in patients with IPF. For this IRB-approved HIPAA-compliant retrospective single-center study, adult patients with IPF and HRCT imaging (N = 50) and an age- and gender-matched negative control group with normal HRCT imaging (N = 50) were identified. Four independent readers measured the PSI score in the midlungs on HRCT images using dedicated software while blinded to clinical data. At-test w...
Source: Journal of Digital Imaging - September 5, 2023 Category: Radiology Source Type: research
Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7
In conclusion, YOLOv7 outperformed YOLOv3 in car ies detection and increasing the image size did not enhance the model’s performance. Elevating the IoU from 50% to 75% and confidence threshold from 0.001 to 0.5 led to a reduction of the model’s performance, while simultaneously improving precision and reducing recall (minimizing false positive s and negatives) for carious lesion detection in bitewing radiographs. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - August 28, 2023 Category: Radiology Source Type: research
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
AbstractLarge numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled dat...
Source: Journal of Digital Imaging - August 24, 2023 Category: Radiology Source Type: research
Assessing Available Open-Source PACS Options
AbstractMedical imaging technology is producing a growing number of medical images types as well as patient-related information. The benefits of using modern medical imaging systems in healthcare are undeniable. Picture archiving and communication system (PACS) have revolutionized medical imaging practice. PACS have widely impacted the accessibility of medical images, reduced imaging costs, eliminated the physical storage of films, improved time management of radiologists, and allowed automated decision-making and diagnosis. Many health organizations and manufacturers have invested on developing commercial PACS. However, c...
Source: Journal of Digital Imaging - August 17, 2023 Category: Radiology Source Type: research
Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-b...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
