Exploring Radiomics Features Based on H & amp;E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study
This study provides important biomarkers for accurate diagnosis of muscle atrophy. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - April 23, 2024 Category: Radiology Source Type: research
TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation
AbstractBiomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment approaches. Nowadays, self-attention-based networks have achieved competitive performance in both natural language processing and computer vision, but the computational cost has reduced their popularity in practical applications. The recent study of Convolutional Neural Network (CNN) explores linear functions within modified CNN layer demonstrating pure CNN-based networks can still achieve competitive results against Vision Transformer (ViT) in biomedical image segmentation, wi...
Source: Journal of Digital Imaging - April 23, 2024 Category: Radiology Source Type: research
Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository
AbstractIn this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a m...
Source: Journal of Digital Imaging - April 23, 2024 Category: Radiology Source Type: research
An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation
AbstractStructural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two n...
Source: Journal of Digital Imaging - April 22, 2024 Category: Radiology Source Type: research
Real-Time Optimal Synthetic Inversion Recovery Image Selection (RT-OSIRIS) for Deep Brain Stimulation Targeting
AbstractDeep brain stimulation (DBS) is a method of electrical neuromodulation used to treat a variety of neuropsychiatric conditions including essential tremor, Parkinson ’s disease, epilepsy, and obsessive–compulsive disorder. The procedure requires precise placement of electrodes such that the electrical contacts lie within or in close proximity to specific target nuclei and tracts located deep within the brain. DBS electrode trajectory planning has become incr easingly dependent on direct targeting with the need for precise visualization of targets. MRI is the primary tool for direct visualization, and this has led...
Source: Journal of Digital Imaging - April 19, 2024 Category: Radiology Source Type: research
Multimodality Fusion Strategies in Eye Disease Diagnosis
In conclusion, this study substantiates late f usion as the optimal strategy for eye disease diagnosis compared to early and joint fusion, showcasing its superiority in leveraging multimodal information. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - April 19, 2024 Category: Radiology Source Type: research
Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement
AbstractThe left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. ...
Source: Journal of Digital Imaging - April 19, 2024 Category: Radiology Source Type: research
The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks
AbstractWe aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process uti...
Source: Journal of Digital Imaging - April 18, 2024 Category: Radiology Source Type: research
Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model
This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those ...
Source: Journal of Digital Imaging - April 18, 2024 Category: Radiology Source Type: research
A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography
AbstractArchitectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18 –45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting a rchitectural distortion in mammography images by combining preprocessing methods and a novel structure fusion...
Source: Journal of Digital Imaging - April 16, 2024 Category: Radiology Source Type: research
Skin Cancer Image Segmentation Based on Midpoint Analysis Approach
This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95...
Source: Journal of Digital Imaging - April 16, 2024 Category: Radiology Source Type: research
Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma
The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interes...
Source: Journal of Digital Imaging - April 16, 2024 Category: Radiology Source Type: research
Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising
AbstractConvolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network. Transformers have emerged as an approach aiming to address and overcome these specific limitations of CNNs in the context of medical image ...
Source: Journal of Digital Imaging - April 15, 2024 Category: Radiology Source Type: research
Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte
This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification p...
Source: Journal of Digital Imaging - April 15, 2024 Category: Radiology Source Type: research
An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images
AbstractSpine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM),...
Source: Journal of Digital Imaging - April 15, 2024 Category: Radiology Source Type: research
