image processing

Latest posts

This blur detection model could provide instantaneous feedback to technicians

European Radiology Experimental March 2025

This study developed a model to automatically detect blurred areas in mammograms, which can affect diagnostic accuracy. Using a retrospective dataset consisting of 152 mammograms from three vendors, expert radiologists outlined blurred regions. Normalized Wiener spectra (nWS) were extracted and processed through a convolutional neural network (CNN) to classify images as either blurred or sharp. The model showed an AUROC

Complexities of deep learning-based undersampled MR image reconstruction

Constant Richard Noordman April 2024

Recent advances in AI have led to deep learning-based MR undersampled image reconstruction methods showing more speed-ups compared to traditional algorithms. MR undersampling is an excellent way to reduce scan time but can negatively impact image quality. Our literature review aims to inform a broader audience about this complex topic. This highly multidisciplinary science requires the informed input of many

AI for prostate MRI: open datasets, available applications, and grand challenges

European Radiology Experimental April 2023

This narrative review provides an overview of the current state-of-the-art artificial intelligence (AI) applications for prostate MRI by focusing on open datasets, commercially and publically available AI systems, and challenges. The authors state that large amounts of research are still required in order to successfully utilize AI in the whole prostate pathway. Due to the rapidly growing field, continuous up-to-date

The potential of texture analysis for breast density classification

European Radiology Experimental August 2022

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 factor for developing breast cancer is breast density (BD). Epidemiological studies show that women with dense tissue may have an increased risk of developing breast cancer by 2-6 times when compared to women with less

Creating a training set for AI from initial segmentations of airways

Ivan Dudurych April 2022

An important challenge in the use of artificial intelligence (AI) for medical image segmentation tasks is the lack of high-quality, scan protocol-specific datasets. AI performs best on narrow tasks with homogenous specifications. Thus, pre-trained models may be inadequate for use in centre-specific studies if the scan protocols do not match. For the airway segmentation task in the Imaging in Lifelines

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

European Radiology August 2021

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 of the physiological and diseased aorta in computed tomography (CT) angiographies. The authors were able to determine that automated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and accurate, relating

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

European Radiology March 2021

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 combining image standardization with ComBat realignment, thus demonstrating that this could efficiently remove the scanner/protocol effect while preserving the individual variations in phantom, brain, and prostate MR scans. Key points Radiomic feature values obtained using

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

Raquel Perez-Lopez February 2021

Medical imaging encodes information of underlying tissues and can provide a comprehensive view of the entire body repeatedly throughout the course of disease. Therefore, medical imaging has been the foundation of disease detection and follow-up in clinical practice for decades. However, to date, observer-dependent evaluation of medical imaging has been a constraint for developing imaging biomarkers towards precision medicine. The

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

European Radiology August 2021

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

European Radiology March 2021

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

Raquel Perez-Lopez February 2021

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

Latest posts

This blur detection model could provide instantaneous feedback to technicians

Complexities of deep learning-based undersampled MR image reconstruction

AI for prostate MRI: open datasets, available applications, and grand challenges

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

This blur detection model could provide instantaneous feedback to technicians

Complexities of deep learning-based undersampled MR image reconstruction

AI for prostate MRI: open datasets, available applications, and grand challenges

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

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Footnotes:

image processing

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

Latest posts

Become A Member Today!

Check out our different membership options.

Membership

Free membership

ESR Friends

Footnotes:

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