AIMC Topic: Tomography, X-Ray Computed
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Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans.
La Radiologia medica
Aug 9, 2024
PURPOSE: To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).
Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.
Journal of applied clinical medical physics
Aug 9, 2024
BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed fo...
A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.
Cancer science
Aug 9, 2024
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...
Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.
Journal of medical imaging and radiation oncology
Aug 9, 2024
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...
Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.
Scientific reports
Aug 9, 2024
Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include dedu...
Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction.
Physics in medicine and biology
Aug 9, 2024
Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied p...
A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.
AJNR. American journal of neuroradiology
Aug 9, 2024
BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imagi...
CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervis...
Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.
Journal of imaging informatics in medicine
Aug 8, 2024
To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 8...
Quantitative Three-Dimensional Imaging Analysis of HfO Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.
ACS nano
Aug 8, 2024
It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitiviti...
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