AIMC Topic: Image Processing, Computer-Assisted

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Mutual enhancing learning-based automatic segmentation of CT cardiac substructure.

Physics in medicine and biology
Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can...

MultiHeadGAN: A deep learning method for low contrast retinal pigment epithelium cell segmentation with fluorescent flatmount microscopy images.

Computers in biology and medicine
BACKGROUND: Retinal pigment epithelium (RPE) aging is an important cause of vision loss. As RPE aging is accompanied by changes in cell morphological features, an accurate segmentation of RPE cells is a prerequisite to such morphology analyses. Due t...

A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images.

Computer methods and programs in biomedicine
Previously, doctors interpreted computed tomography (CT) images based on their experience in diagnosing kidney diseases. However, with the rapid increase in CT images, such interpretations were required considerable time and effort, producing inconsi...

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best ...

Image Sensing and Processing with Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...].

DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.

PloS one
Development of automated analysis tools for "single ion channel" recording is hampered by the lack of available training data. For machine learning based tools, very large training sets are necessary with sample-by-sample point labelled data (e.g., 1...

Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network.

PloS one
BACKGROUND: Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation...

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and n...

A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images.

Computer methods and programs in biomedicine
This paper proposes an encoder-decoder architecture for kidney segmentation. A hyperparameter optimization process is implemented, including the development of a model architecture, selecting a windowing method and a loss function, and data augmentat...

Automatic microscopic diagnosis of diseases using an improved UNet++ architecture.

Tissue & cell
Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system based on microscopic image processing of patient tissue samples for the automatic diagnosis of anthr...