AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiase...

Deep Learning on chromatographic data for Segmentation and Sensitive Analysis.

Journal of chromatography. A
Lateral flow immunoassay (LFIA) is one of the most common methods in point-of-care testing, which is widely applied in some conditions for various applications. Image segmentation is an increasingly popular experimental paradigm to efficiently test t...

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Scientific reports
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development an...

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Physics in medicine and biology
Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive techn...

Half2Half: deep neural network based CT image denoising without independent reference data.

Physics in medicine and biology
Reducing radiation dose of x-ray computed tomography (CT) and thereby decreasing the potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been proposed to reduce noise in low-dose CT (LdCT) images and showed promising...

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Physics in medicine and biology
Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic les...

Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images.

BioMed research international
Cross-modality medical image synthesis between magnetic resonance (MR) images and computed tomography (CT) images has attracted increasing attention in many medical imaging area. Many deep learning methods have been used to generate pseudo-MR/CT imag...

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images.

The International journal of neuroscience
BACKGROUND AND OBJECTIVES: Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 1...

Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landm...

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of in Relation to Hydrographic Conditions.

Sensors (Basel, Switzerland)
Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolut...