AIMC Topic: Tomography, X-Ray Computed
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Applications of deep learning in trauma radiology: A narrative review.
Biomedical journal
Apr 26, 2024
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segm...
Deep Learning for Automated Image Segmentation of the Middle Ear: A Scoping Review.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Apr 26, 2024
OBJECTIVE: Convolutional neural networks (CNNs) have revolutionized medical image segmentation in recent years. This scoping review aimed to carry out a comprehensive review of the literature describing automated image segmentation of the middle ear ...
Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.
The American journal of gastroenterology
Apr 25, 2024
INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography ...
Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.
Medical & biological engineering & computing
Apr 25, 2024
The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel aut...
A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 25, 2024
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or the...
Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.
Computers in biology and medicine
Apr 25, 2024
This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentatio...
Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.
Journal of applied clinical medical physics
Apr 25, 2024
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...
Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.
BMC pulmonary medicine
Apr 25, 2024
BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces ch...
Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach.
Diagnostic and interventional radiology (Ankara, Turkey)
Apr 24, 2024
PURPOSE: Spontaneous intracerebral hemorrhage (ICH) is the most severe form of stroke. The timely assessment of early hematoma enlargement and its proper treatment are of great significance in curbing the deterioration and improving the prognosis of ...
Multi-modal medical image fusion using improved dual-channel PCNN.
Medical & biological engineering & computing
Apr 24, 2024
This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a...
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