AIMC Topic: Image Processing, Computer-Assisted

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White blood cells detection and classification based on regional convolutional neural networks.

Medical hypotheses
White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Ba...

Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs.

International journal of computer assisted radiology and surgery
PURPOSE: Convolutional neural networks (CNNs) have obtained enormous success in liver segmentation. However, there are several challenges, including low-contrast images, and large variations in the shape, and appearance of the liver. Incorporating pr...

Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network.

Journal of healthcare engineering
Breast cancer is the most common cancer among women worldwide with about half a million cases reported each year. Mammary thermography can offer early diagnosis at low cost if adequate thermographic images of the breasts are taken. The identification...

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

NeuroImage
MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely availab...

An iterative multi-path fully convolutional neural network for automatic cardiac segmentation in cine MR images.

Medical physics
PURPOSE: Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information ...

Stomach Deformities Recognition Using Rank-Based Deep Features Selection.

Journal of medical systems
Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for...

A deep learning reconstruction framework for X-ray computed tomography with incomplete data.

PloS one
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite oft...

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

International journal of computer assisted radiology and surgery
PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is ...

[Digital Image Processing and Deep Neural Networks in Ophthalmology - Current Trends].

Klinische Monatsblatter fur Augenheilkunde
The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodologica...

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning.

Medical physics
PURPOSE: To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented.