Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification
AbstractCommon pediatric distal forearm fractures necessitate precise detection. To support prompt treatment planning by clinicians, our study aimed to create a multi-class convolutional neural network (CNN) model for pediatric distal forearm fractures, guided by the AO Foundation/Orthopaedic Trauma Association (AO/ATO) classification system for pediatric fractures. The GRAZPEDWRI-DX dataset (2008 –2018) of wrist X-ray images was used. We labeled images into four fracture classes (FRM, FUM, FRE, and FUE with F, fracture; R, radius; U, ulna; M, metaphysis; and E, epiphysis) based on the pediatric AO/ATO classification. We...
Source: Journal of Digital Imaging - February 2, 2024 Category: Radiology Source Type: research
Evaluation of Mucosal Healing in Crohn ’s Disease: Radiomics Models of Intestinal Wall and Mesenteric Fat Based on Dual-Energy CT
This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn ’s disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iod ine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from...
Source: Journal of Digital Imaging - February 1, 2024 Category: Radiology Source Type: research
An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network
This study explores the concept of data augmentation by applying the methods motivated via commonly used image augmentation techniques. According to the experimental findings, the results of the AF are closely related to the otolaryngologists ’ ratings and are useful for preoperative planning, intraoperative decision-making, and postoperative assessment. Furthermore, the visualization indicates that the proposed AF is capable of predicting the nasal base symmetry and capturing asymmetry areas to facilitate semantic predictions. The co des are accessible athttps://github.com/AshooriMaryam/Nasal-Aesthetic-Assessment-Deep-...
Source: Journal of Digital Imaging - January 29, 2024 Category: Radiology Source Type: research
Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels
This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength lev els. Ninety-fo...
Source: Journal of Digital Imaging - January 29, 2024 Category: Radiology Source Type: research
Review of the Free Research Software for Computer-Assisted Interventions
AbstractResearch software is continuously developed to facilitate progress and innovation in the medical field. Over time, numerous research software programs have been created, making it challenging to keep abreast of what is available. This work aims to evaluate the most frequently utilized software by the computer-assisted intervention (CAI) research community. The software assessments encompass a range of criteria, including load time, stress load, multi-tasking, extensibility and range of functionalities, user-friendliness, documentation, and technical support. A total of eight software programs were selected: 3D Slic...
Source: Journal of Digital Imaging - January 29, 2024 Category: Radiology Source Type: research
MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas
AbstractThe purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-...
Source: Journal of Digital Imaging - January 24, 2024 Category: Radiology Source Type: research
Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model
In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perce...
Source: Journal of Digital Imaging - January 18, 2024 Category: Radiology Source Type: research
Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks
AbstractThis work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using convolutional neural networks (CNNs), seeking to enhance and fasten clinician diagnosis. We implemented three single-view 2D Attention U-Nets with 3D view integration and trained them to automatically segment the aortic root and coronary arteries of 124 computed tomography angiographies (CTAs), with normal coronaries or AAOCA. Furthermore, we automatically classified the segmented geometries as normal or AAOCA using a decision tree model. For CTAs in the test set (n = 1...
Source: Journal of Digital Imaging - January 17, 2024 Category: Radiology Source Type: research
Development and Validation of a 3D Resnet Model for Prediction of Lymph Node Metastasis in Head and Neck Cancer Patients
This study enrolled 156 head and neck cancer patients and analyzed 342 lymph nodes segmented from surgical pathologic reports. The patients ’ clinical and pathological data related to the primary tumor site and clinical and pathology T and N stages were collected. To predict LNM, we developed a dual-pathway 3D Resnet model incorporating two Resnet models with different depths to extract features from the input data. To assess the mode l’s performance, we compared its predictions with those of radiologists in a test dataset comprising 38 patients. The study found that the dimensions and volume of LNM + were signific...
Source: Journal of Digital Imaging - January 16, 2024 Category: Radiology Source Type: research
Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases
AbstractAccurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung...
Source: Journal of Digital Imaging - January 16, 2024 Category: Radiology Source Type: research
Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear
This study aims to overcome the challenges brought by small and imbalanced data and achieve fast and accurate ACL tear classification based on magnetic resonance imaging (MRI) of the knee. We propose a lightweight attentive graph neural network (GNN) with a conditional random field (CRF), named the ACGNN, to classify ACL ruptures in knee MR images. A metric-based meta-learning strategy is introduced to conduct independent testing through multiple node classification tasks. We design a lightweight feature embedding network using a feature-based knowledge distillation method to extract features from the given images. Then, G...
Source: Journal of Digital Imaging - January 16, 2024 Category: Radiology Source Type: research
The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations
This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segmentation of four types of uterine lesions and three types of normal tissues on magnetic resonance images. The MRI data of the patients were retrospectively collected from the database of our institution, and the T2-weighted sequence images were selected and only image-level annotations were made. The proposed two-stage model can be divided into four sequential parts: the pixel correlation module, the class re-activation map module, the inter-pixel relation network module, and the Deeplab v3 + module. The di...
Source: Journal of Digital Imaging - January 16, 2024 Category: Radiology Source Type: research
Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model
AbstractDeep-learning (DL) algorithms have the potential to change medical image classification and diagnostics in the coming decade. Delayed diagnosis and treatment of avascular necrosis (AVN) of the lunate may have a detrimental effect on patient hand function. The aim of this study was to use a segmentation-based DL model to diagnose AVN of the lunate from wrist postero-anterior radiographs. A total of 319 radiographs of the diseased lunate and 1228 control radiographs were gathered from Helsinki University Central Hospital database. Of these, 10% were separated to form a test set for model validation. MRI confirmed the...
Source: Journal of Digital Imaging - January 16, 2024 Category: Radiology Source Type: research
Polyp Segmentation Using a Hybrid Vision Transformer and a Hybrid Loss Function
AbstractAccurate and early detection of precursor adenomatous polyps and their removal at the early stage can significantly decrease the mortality rate and the occurrence of the disease since most colorectal cancer evolve from adenomatous polyps. However, accurate detection and segmentation of the polyps by doctors are difficult mainly these factors: (i) quality of the screening of the polyps with colonoscopy depends on the imaging quality and the experience of the doctors; (ii) visual inspection by doctors is time-consuming, burdensome, and tiring; (iii) prolonged visual inspections can lead to polyps being missed even wh...
Source: Journal of Digital Imaging - January 12, 2024 Category: Radiology Source Type: research
Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety
AbstractFlagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence or absence of an aneurysm clip on CT images. A total of 280 CT head scans were collected, 140 with aneurysm clips visible and 140 without. The data were used to retrain a pre-trained image classification neural network to classify CT localizer images. Models were developed using fivefold cross-validation and then tested ...
Source: Journal of Digital Imaging - January 12, 2024 Category: Radiology Source Type: research
