image processing

The potential of texture analysis for breast density classification

Breast cancer continues to be the most commonly diagnosed cancer among women with over 2 million new cases per year worldwide. One important independent risk...

European Radiology Experimental

8 months ago

The potential of texture analysis for breast density classification

Creating a training set for AI from initial segmentations of airways

An important challenge in the use of artificial intelligence (AI) for medical image segmentation tasks is the lack of high-quality, scan protocol-specific...

Ivan Dudurych

11 months ago

Creating a training set for AI from initial segmentations of airways

Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach

Performed between 2015 and 2018, the purpose of this study was to develop and validate a deep learning-based algorithm for the segmentation and quantification...

European Radiology

2 years ago

Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

In this study, the authors extended the ComBat approach to provide a harmonization procedure that is applicable to any radiomic feature. They achieve this by...

European Radiology

2 years ago

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Medical imaging encodes information of underlying tissues and can provide a comprehensive view of the entire body repeatedly throughout the course of disease....

Raquel Perez-Lopez

2 years ago

image processing

The potential of texture analysis for breast density classification

Creating a training set for AI from initial segmentations of airways

Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Welcome to the blog on Artificial Intelligence of the European Society of Radiology

Latest posts

The potential of texture analysis for breast density classification

Creating a training set for AI from initial segmentations of airways

Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Welcome to the blog on Artificial Intelligence of the European Society of Radiology

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