AIMC Topic: Tomography, X-Ray Computed
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The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance.
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Apr 17, 2024
PURPOSE: The aim of our work was to demonstrate the importance of artificial intelligence-based analysis of fractional flow reserves of computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance in patients ...
Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.
Medical physics
Apr 17, 2024
BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is us...
Robot-assisted anterior cruciate ligament reconstruction based on three-dimensional images.
Journal of orthopaedic surgery and research
Apr 17, 2024
Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image...
Artificial Intelligence With Deep Learning Enables Assessment of Aortic Aneurysm Diameter and Volume Through Different Computed Tomography Phases.
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
Apr 16, 2024
The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.
Acta radiologica (Stockholm, Sweden : 1987)
Apr 16, 2024
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.
Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.
Journal of imaging informatics in medicine
Apr 15, 2024
Convolutional 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 en...
An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.
Journal of imaging informatics in medicine
Apr 15, 2024
Spine 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 sev...
Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.
F1000Research
Apr 15, 2024
BACKGROUND: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and...
Unsupervised model adaptation for source-free segmentation of medical images.
Medical image analysis
Apr 14, 2024
The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when task...
HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.
Computer methods and programs in biomedicine
Apr 14, 2024
BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular for...
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