DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus
AbstractDepth of focus (DOF) is defined as the axial range in which the specimen stage moves without losing focus while the imaging apparatus remains stable. It may not be possible to capture an image that includes the entire specimen in focus due to the narrow DOF in microscopic systems. Extended depth of focus (EDOF) is used to overcome this limitation in microscopic systems. Although the researchers have developed so many EDOF microscope approaches, this research field still has some crucial shortcomings such as high computational costs, complexity and execution time, requiring additional equipment, low precise characte...
Source: Journal of Digital Imaging - March 25, 2024 Category: Radiology Source Type: research
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction
AbstractDeep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation technique...
Source: Journal of Digital Imaging - March 21, 2024 Category: Radiology Source Type: research
Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images
This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-...
Source: Journal of Digital Imaging - March 19, 2024 Category: Radiology Source Type: research
Reading Room Interruptions are Less Disruptive When Using Asynchronous Communication Methods
AbstractRadiologist interruptions, though often necessary, can be disruptive. Prior literature has shown interruptions to be frequent, occurring during cases, and predominantly through synchronous communication methods such as phone or in person causing significant disengagement from the study being read. Asynchronous communication methods are now more widely available in hospital systems such as ours. Considering the increasing use of asynchronous communication methods, we conducted an observational study to understand the evolving nature of radiology interruptions. We hypothesize that compared to interruptions occurring ...
Source: Journal of Digital Imaging - March 19, 2024 Category: Radiology Source Type: research
Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model
AbstractThe incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of strategies to control the disease and the selection of an appropriate treatment pathway. Given the necessity for broader COVID-19 diagnosis, researchers have developed more advanced, rapid, and efficient detection methods. By conducting an initial comparative analysis of various widely used convolutional neural network (CNN) models, we determine an appropriate CNN model....
Source: Journal of Digital Imaging - March 18, 2024 Category: Radiology Source Type: research
Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs
This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants. Transfer learning of a pre-trained machine learning model was employed for lung region segmentation and image fusion for BPD prediction to enhance the performance of the AI model. The lung segmentation model uses transfer learning to achieve a dice score ...
Source: Journal of Digital Imaging - March 18, 2024 Category: Radiology Source Type: research
An Exploratory Pilot Study on the Application of Radiofrequency Ablation for Atrial Fibrillation Guided by Computed Tomography-Based 3D Printing Technology
In this study, a total of 122 patients were included, with 53 allocated to the 3DP group and 69 to the control group. The analysis of the morphological measurements of the LA and PV taken from the workstation or direct entity measurement showed no significant difference between the two groups (P > 0.05). However, patients in the 3DP group experienced significantly shorter RFA times (97.03 ± 28.39 compared to 120.51 ± 44.76 min,t = 3.05,P = 0.003), reduced duration of radiation exposure (2.55 [interquartile range 2.01, 3.24] versus 3.20 [2.28, 3.91] min,Z = 3.23,P < 0.001), and shorter...
Source: Journal of Digital Imaging - March 15, 2024 Category: Radiology Source Type: research
An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT
We present a novel deep cascade processing workflow to achieve diagnostic visualization on LR-ULDCT (<0.3 mSv) at par high-resolution CT (HRCT) of 100 mSV radiation technology. To this end, we build a low-risk and affordable deep cascade network comprising three sequential deep processes: restoration, super-resolution (SR), and segmentation. Given degraded LR-ULDCT, the first novel network unsupervisedly learns restoration function from augmenting patch-based dictionaries and residuals. The restored version is then super-resolved (SR) for target (sensor) resolution. Here, we combine perceptual and adversarial losses in ...
Source: Journal of Digital Imaging - March 15, 2024 Category: Radiology Source Type: research
Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods
AbstractOur study aims to evaluate the potential of a deep learning (DL) algorithm for differentiating the signal intensity of bone marrow between osteomyelitis (OM), Charcot neuropathic osteoarthropathy (CNO), and trauma (TR). The local ethics committee approved this retrospective study. From 148 patients, segmentation resulted in 679 labeled regions for T1-weighted images (comprising 151 CNO, 257 OM, and 271 TR) and 714 labeled regions for T2-weighted images (consisting of 160 CNO, 272 OM, and 282 TR). We employed both multi-class classification (MCC) and binary-class classification (BCC) approaches to compare the classi...
Source: Journal of Digital Imaging - March 15, 2024 Category: Radiology Source Type: research
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
AbstractRadiology narrative reports often describe characteristics of a patient ’s disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image analysis algorithms. We proposed a novel vision-language model, ConTEXTual Net, for the task of pneumothorax segmentatio n on chest radiographs. ConTEXTual Net extracts language features from physician-generated free-form radiology reports using a pre-trained language model. We then introduced cross-attention between the language features and the intermediate embeddin...
Source: Journal of Digital Imaging - March 14, 2024 Category: Radiology Source Type: research
A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images
AbstractDeep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance be...
Source: Journal of Digital Imaging - March 14, 2024 Category: Radiology Source Type: research
Checklist for Reproducibility of Deep Learning in Medical Imaging
AbstractThe application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 expe...
Source: Journal of Digital Imaging - March 14, 2024 Category: Radiology Source Type: research
Accuracy Analysis of 3D Bone Fracture Models: Effects of Computed Tomography (CT) Imaging and Image Segmentation
In conclusion, this study demonstrates that 3D bone fracture models can be obtained with clinical routine scanners and scan protocols, utilizing a simple global segmentation threshold, thereby providing an accurate and reliable tool for pre-operative planning. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - March 14, 2024 Category: Radiology Source Type: research
GA-UNet: A Lightweight Ghost and Attention U-Net for Medical Image Segmentation
AbstractU-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide range of applications. However, these variants primarily focus on enhancing the model ’s feature extraction capabilities, often resulting in increased parameters and floating point operations (Flops). In this paper, we propose GA-UNet (Ghost and Attention U-Net), a lightweight U-Net for medical image segmentation. GA-UNet consists mainly of lightweight GhostV2 bottlenecks that redu ce redundant information and Convolutional Block Attention Modules that capture key feat...
Source: Journal of Digital Imaging - March 13, 2024 Category: Radiology Source Type: research
Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features
AbstractBreast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the compar...
Source: Journal of Digital Imaging - March 13, 2024 Category: Radiology Source Type: research
