AIMC Topic: Image Processing, Computer-Assisted

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A Review of Image Processing Techniques for Deepfakes.

Sensors (Basel, Switzerland)
Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques ...

RA V-Net: deep learning network for automated liver segmentation.

Physics in medicine and biology
Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical ...

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images.

BioMed research international
Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system's anatomical abnormalities. Standardized cerebellar assessments from la...

Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities.

Computational and mathematical methods in medicine
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In rece...

Deep Learning-Based CT Imaging for the Diagnosis of Liver Tumor.

Computational intelligence and neuroscience
The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liver tumors. Fifty-eight patients with liver tumors were selected, and their CT images were segmented ...

Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency.

Medical image analysis
Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. Semi-supervised...

Adapting a low-count acquisition of the bone scintigraphy using deep denoising super-resolution convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Deep-layer learning processing may improve contrast imaging with greater precision in low-count acquisition. However, no data on noise reduction using super-resolution processing for deep-layer learning have been reported in nuclear medicine...

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

European radiology experimental
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds gr...

Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks.

Physics in medicine and biology
. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment ...