AIMC Topic: Image Processing, Computer-Assisted

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Deep learning enables pathologist-like scoring of NASH models.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) and the progressive form of non-alcoholic steatohepatitis (NASH) are diseases of major importance with a high unmet medical need. Efficacy studies on novel compounds to treat NAFLD/NASH using disease models a...

Removal of computed tomography ring artifacts via radial basis function artificial neural networks.

Physics in medicine and biology
Ring artifacts in computed tomography (CT) images are caused by the undesirable response of detector pixels, which leads to the degradation of CT images. Accordingly, it affects the image interpretation, post-processing, and quantitative analysis. In...

CNN-based PET sinogram repair to mitigate defective block detectors.

Physics in medicine and biology
Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component fail...

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots.

PloS one
Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. ...

Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions.

The International journal of neuroscience
We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get th...

Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips.

Tissue & cell
Despite profound knowledge of the incidence of oral cancers and a large body of research beyond it, it continues to beat diagnosis and treatment management. Post physical observation by clinicians, a biopsy is a gold standard for accurate detection o...

DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.

Scientific reports
The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The siz...

ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images.

Journal of chemical information and modeling
The rise of data science is leading to new paradigms in data-driven materials discovery. This carries an essential notion that large data sources containing chemical structure and property information can be mined in a fashion that detects and exploi...

CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.

Medical physics
PURPOSE: Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy...

Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.

Medical image analysis
Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is lab...