AIMC Topic: Tomography, X-Ray Computed
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Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.
NeuroImage
Nov 16, 2024
INTRODUCTION: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this ex...
Preoperative prediction of post hepatectomy liver failure after surgery for hepatocellular carcinoma on CT-scan by machine learning and radiomics analyses.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Nov 15, 2024
INTRODUCTION: No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learnin...
Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.
Japanese journal of radiology
Nov 14, 2024
PURPOSE: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hy...
BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis.
Computer methods and programs in biomedicine
Nov 14, 2024
BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, du...
Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.
La Radiologia medica
Nov 14, 2024
BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpre...
Automatic localization and deep convolutional generative adversarial network-based classification of focal liver lesions in computed tomography images: A preliminary study.
Journal of gastroenterology and hepatology
Nov 14, 2024
BACKGROUND AND AIM: Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. We developed a deep learning-based localization and classification (DLLC) system for focal liver les...
Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.
Journal of applied clinical medical physics
Nov 14, 2024
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Organs at Risk
Radiotherapy Planning, Computer-Assisted
Radiation Dosage
Signal-To-Noise Ratio
Deep Learning
Image Processing, Computer-Assisted
Radiographic Image Interpretation, Computer-Assisted
Feasibility Studies
Abdominal Neoplasms
Radiotherapy, Image-Guided
Radiographic Image Enhancement
Pelvic Neoplasms
Radiotherapy Dosage
Radiotherapy, Intensity-Modulated
Phantoms, Imaging
Humans
Tomography, X-Ray Computed
Female
Male
Algorithms
Prospective Studies
SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection.
Computers in biology and medicine
Nov 13, 2024
OBJECTIVES: By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolism has been improved, leading to more accurate identification of this condition. This advancement aims to better serve medical diagnosis and treatment.
Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.
Oral surgery, oral medicine, oral pathology and oral radiology
Nov 12, 2024
OBJECTIVE: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model ...
Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.
Clinical imaging
Nov 12, 2024
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...
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