AIMC Topic: Abdomen

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Medical lesion segmentation by combining multimodal images with modality weighted UNet.

Medical physics Apr 7, 2022
PURPOSE: Automatic segmentation of medical lesions is a prerequisite for efficient clinic analysis. Segmentation algorithms for multimodal medical images have received much attention in recent years. Different strategies for multimodal combination (o...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Abdomen Liver Algorithms Tomography, X-Ray Computed
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Clinical practice guideline for body composition assessment based on upper abdominal magnetic resonance images annotated using artificial intelligence.

Chinese medical journal Mar 20, 2022
Artificial Intelligence Body Composition Abdomen Practice Guidelines as Topic Magnetic Resonance Imaging
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Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

European radiology Mar 3, 2022
OBJECTIVES: To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR).
Radiation Dosage Algorithms Tomography, X-Ray Computed Abdomen Iodine Radiographic Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Humans Deep Learning
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A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans.

Medical physics Feb 22, 2022
PURPOSE: Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproduci...
Neural Networks, Computer Tomography, X-Ray Computed Abdomen Kidney Calculi Deep Learning Humans
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Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.

European radiology Feb 16, 2022
OBJECTIVES: To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST).
Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Algorithms Tomography, X-Ray Computed Humans Deep Learning Abdomen Aged
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General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

Medical physics Feb 7, 2022
PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-establi...
Deep Learning Image Processing, Computer-Assisted Abdomen Humans Male Organs at Risk Radiotherapy Planning, Computer-Assisted Head and Neck Neoplasms Pelvis
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Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

PloS one Jan 21, 2022
OBJECTIVES: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.
Male Deep Learning Child Image Processing, Computer-Assisted Child, Preschool Signal-To-Noise Ratio Tomography, X-Ray Computed Radiography, Abdominal Abdomen Female Humans Radiation Dosage Algorithms
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Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography.

Computer methods and programs in biomedicine Dec 25, 2021
PURPOSE: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, A...
Pelvis Tomography, X-Ray Computed Machine Learning Abdomen Algorithms
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Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets.

Journal of digital imaging Dec 17, 2021
Organ segmentation from existing imaging is vital to the medical image analysis and disease diagnosis. However, the boundary shapes and area sizes of the target region tend to be diverse and flexible. And the frequent applications of pooling operatio...
Abdomen Humans Tomography, X-Ray Computed Pancreas Neural Networks, Computer Image Processing, Computer-Assisted
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The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.

European radiology Dec 15, 2021
OBJECTIVE: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).
Humans Algorithms Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Deep Learning Densitometry Abdomen
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