AIMC Topic: Thorax

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Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.

Journal of computer assisted tomography Jun 30, 2023
OBJECTIVE: Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning-based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating th...
Thorax Phantoms, Imaging Neural Networks, Computer Tomography, X-Ray Computed Deep Learning Humans Image Processing, Computer-Assisted
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An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation.

IEEE journal of biomedical and health informatics Jun 30, 2023
Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardiovascular diseases and in 3D modeling of the heart. Deep convolutional neural networks have been demonstrated to achieve state-of-the-art performance i...
Humans Neural Networks, Computer Heart Thorax Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

European radiology May 26, 2023
OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method.
Deep Learning Thorax Male Middle Aged Costal Cartilage Retrospective Studies Tomography, X-Ray Computed Female Adult Humans
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Deep learning to estimate lung disease mortality from chest radiographs.

Nature communications May 16, 2023
Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limite...
Lung Deep Learning Humans Thorax Lung Diseases Radiography, Thoracic
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Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images.

PloS one May 9, 2023
PURPOSE: To improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN).
Healthy Volunteers Humans Semantic Web Thorax Neural Networks, Computer Lung
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Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction.

European radiology Apr 18, 2023
OBJECTIVES: Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and ...
Thorax Tomography, X-Ray Computed Humans Bronchi Artificial Intelligence Reproducibility of Results
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Deformable registration of lung 3DCT images using an unsupervised heterogeneous multi-resolution neural network.

Medical & biological engineering & computing Apr 18, 2023
Lung image registration is more challenging than other organs. This is because the breath of the human body causes large deformations in the lung parenchyma and small deformations in tissues such as the pulmonary vascular. Many studies have recently ...
Algorithms Lung Image Processing, Computer-Assisted Thorax Humans Neural Networks, Computer
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Collaborative training of medical artificial intelligence models with non-uniform labels.

Scientific reports Apr 13, 2023
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have prov...
Thorax Computer Simulation Health Facilities Artificial Intelligence Algorithms
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Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Computer methods and programs in biomedicine Mar 22, 2023
BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account...
Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Artificial Intelligence Humans Thorax
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Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.

Medical physics Mar 17, 2023
BACKGROUND: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potent...
Radiography Thorax Tomography, X-Ray Computed Artificial Intelligence Image Processing, Computer-Assisted
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