AIMC Topic: Tomography, X-Ray Computed

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Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

Diagnostic and interventional imaging Sep 14, 2024
PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of autom...
Male Middle Aged Pancreatic Neoplasms Radiographic Image Interpretation, Computer-Assisted Adult Sensitivity and Specificity Algorithms Radiomics Female Retrospective Studies Aged Tomography, X-Ray Computed Aged, 80 and over Neuroendocrine Tumors Young Adult Humans Machine Learning
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Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Sep 13, 2024
Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relie...
Humans Datasets as Topic Pancreas Tomography, X-Ray Computed Deep Learning
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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

BMC medical imaging Sep 13, 2024
BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Middle Aged Aged Lung Neoplasms Neoplasm Invasiveness Adenocarcinoma of Lung Sensitivity and Specificity Radiomics Male
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Automatic 3D pelvimetry framework in CT images and its validation.

Scientific reports Sep 13, 2024
In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial structure and morphology of pelvis, which can be represented by pelvic parameters. Pelvic parameters, including pelvic incidence, pelvic tilt and sacral ...
Pelvimetry Imaging, Three-Dimensional Pelvis Femur Head Humans Algorithms Tomography, X-Ray Computed Female Adult Male Middle Aged Deep Learning
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Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).

Journal of imaging informatics in medicine Sep 12, 2024
The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented...
Pleural Neoplasms Probability Tumor Burden Female Aged Male Middle Aged Convolutional Neural Networks Radiographic Image Interpretation, Computer-Assisted Mesothelioma Humans Neural Networks, Computer Tomography, X-Ray Computed
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Artificial Intelligence-Based Surgery Support Model Using Intraoperative Radiographs for Assessing the Acetabular Component Angle.

The Journal of arthroplasty Sep 12, 2024
BACKGROUND: This study aimed to develop an artificial intelligence-based surgical support model for assessing the acetabular component angle using intraoperative radiographs during total hip arthroplasty and verify its accuracy.
Hip Prosthesis Acetabulum Tomography, X-Ray Computed Radiography Female Surgery, Computer-Assisted Adult Aged Arthroplasty, Replacement, Hip Humans Male Aged, 80 and over Artificial Intelligence Middle Aged
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Skeleton-guided 3D convolutional neural network for tubular structure segmentation.

International journal of computer assisted radiology and surgery Sep 12, 2024
PURPOSE: Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network...
Deep Learning Convolutional Neural Networks Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed
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Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.

European radiology experimental Sep 12, 2024
BACKGROUND: The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (...
Humans Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed Male Middle Aged Deep Learning Ear, Inner Female Aged Adult
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A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Journal of imaging informatics in medicine Sep 11, 2024
Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch a...
Humans Algorithms Deep Learning Tomography, X-Ray Computed Image Processing, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio
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Contrast-Enhanced Computed Tomography-Based Machine Learning Radiomics Predicts IDH1 Expression and Clinical Prognosis in Head and Neck Squamous Cell Carcinoma.

Academic radiology Sep 10, 2024
RATIONALE AND OBJECTIVES: Isocitrate dehydrogenase 1 (IDH1) is a potential therapeutic target across various tumor types. Here, we aimed to devise a radiomic model capable of predicting the IDH1 expression levels in patients with head and neck squamo...
Squamous Cell Carcinoma of Head and Neck Tomography, X-Ray Computed Radiomics Head and Neck Neoplasms Biomarkers, Tumor Humans Female Contrast Media Male Machine Learning Aged Isocitrate Dehydrogenase Middle Aged Prognosis Adult
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