AIMC Journal:
BMC medical imaging

Showing 241 to 250 of 252 articles

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

BMC medical imaging Apr 13, 2021
BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for auto...
Carcinoma, Bronchogenic Male Middle Aged Deep Learning Datasets as Topic Lung Neoplasms Thorax Lymph Nodes Mediastinum Lymphatic Metastasis Contrast Media Axilla Tomography, X-Ray Computed Female Aged Adult Humans Neural Networks, Computer
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High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.

BMC medical imaging Apr 9, 2021
BACKGROUND: Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radi...
Humans Neural Networks, Computer Machine Learning Lung Neoplasms Tomography, X-Ray Computed Solitary Pulmonary Nodule Female Tumor Burden Male False Positive Reactions Middle Aged
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A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging Mar 9, 2021
OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.
Prognosis Retrospective Studies Female Aged Humans Machine Learning Heart Atria Catheter Ablation Recurrence Pulmonary Veins Male Middle Aged ROC Curve Atrial Fibrillation Atrial Appendage
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Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic.

BMC medical imaging Feb 27, 2021
BACKGROUND: Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to va...
Carotid Arteries Magnetic Resonance Angiography Carotid Artery Diseases Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Contrast Media Atherosclerosis
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Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.

BMC medical imaging Feb 16, 2021
BACKGROUND: To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer.
Male Middle Aged ROC Curve Neoadjuvant Therapy Magnetic Resonance Imaging Models, Statistical Rectum Radiographic Image Interpretation, Computer-Assisted Humans Machine Learning Rectal Neoplasms Algorithms Image Interpretation, Computer-Assisted Female Chemoradiotherapy, Adjuvant
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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI.

BMC medical imaging Feb 15, 2021
BACKGROUND: Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only ...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Heart Humans Neural Networks, Computer Software Sensitivity and Specificity
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Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T-weighted Contrast-enhanced Imaging.

BMC medical imaging Feb 3, 2021
BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate th...
Radiation Dosage Disease Progression Adolescent Neoadjuvant Therapy Contrast Media Magnetic Resonance Imaging Glioblastoma Humans Sensitivity and Specificity Aged Image Interpretation, Computer-Assisted Machine Learning Brain Neoplasms Young Adult Retrospective Studies Male Adult Female Middle Aged
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Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BMC medical imaging Jan 28, 2021
BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although...
Image Processing, Computer-Assisted Humans Optic Disk Glaucoma Diagnostic Techniques, Ophthalmological Deep Learning
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MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.

BMC medical imaging Jan 18, 2021
BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medi...
Neural Networks, Computer Kidney Neoplasms Humans Deep Learning Software Design Image Processing, Computer-Assisted Software Validation
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Universal adversarial attacks on deep neural networks for medical image classification.

BMC medical imaging Jan 7, 2021
BACKGROUND: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-...
Pneumonia Radiography, Thoracic Diagnostic Imaging Humans Diabetic Retinopathy Photography Tomography, Optical Coherence Neural Networks, Computer Image Interpretation, Computer-Assisted Skin Neoplasms
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