AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 4361 to 4370 of 5128 articles

Self-supervised learning for low-dose CT image denoising method based on guided image filtering.

Physics in medicine and biology
low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising methods require supervised learning with paired training data that is difficult to obtain. To address t...

Multitask Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer.

Cancer research
UNLABELLED: Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer receiving neoadjuvant chemotherapy, providing timely guidance for clinical dec...

Deep learning-based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning.

Neurosurgical focus
OBJECTIVE: Intracerebral hemorrhage (ICH) remains a critical neurosurgical emergency with high mortality and long-term disability. Despite advancements in minimally invasive techniques, procedural precision remains limited by hematoma complexity and ...

Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Neurosurgical focus
OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine fro...

Zero-shot segmentation of spinal vertebrae with metastatic lesions: an analysis of Meta's Segment Anything Model 2 and factors affecting learning free segmentation.

Neurosurgical focus
OBJECTIVE: Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metastases. Segmenting these metastases is especially challenging given their radiological heterogeneity. C...

Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.

Neurosurgical focus
OBJECTIVE: The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.

Current State of Fibrotic Interstitial Lung Disease Imaging.

Radiology
Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary discussion aided by a thorough radiologic review can achieve a high-confidence diagnosis of ILD in the m...

Deep Learning Models for CT Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study.

Mycoses
BACKGROUND: The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesio...

Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

Clinical transplantation
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Radiology. Imaging cancer
Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communications in Medicine-based deep learning-based denoising (DLD), in evaluating small renal masses (SRM...