On this page, you will find interesting information about the ESR, including facts & figures, details about the executive council, our guiding principles, and much more. Discover everything there is to know about the ESR.

You’re interested in learning more about an ESR membership? Well, you’ve come to the right page. Here, you can discover everything about our memberships, including the benefits and prices. We are sure that you’ll find the perfect option for you.

Part of our mission statement is “Advancing you,” and on this page, you’ll find all of our educational programmes, courses, eBooks, partner links, and downloads, etc., designed to enhance your knowledge and advance your career.

Visit this page for more information about our journals: European Radiology, Insights into Imaging, and European Radiology Experimental, as well as information about Eurorad. Additionally, if you’re interested in publishing your own work, you can find details here.

On this page, patients can find crucial information for their journey towards health, as the European Society of Radiology is at the forefront of advancing patient care and ensuring that individuals receive the assistance they need.

Discover information about our research endeavors and how EIBIR is shaping the future of radiology. Additionally, you’ll find news about research grants, statistics, and details about EIBALL and our Biomarker Inventory.

This is the page for all things ECR and our other events. Learn everything about how to register, submit abstracts, attend in person, become an exhibitor, and much more. There is no congress like ECR. Start your journey here.

Learn more about how the ESR is advancing quality and safety standards in radiology, and access all the information on ESR iGuide, Eurosafe Imaging, our clinical audit tool, as well as ESR-led projects and papers.

Here, you can find information about the ESR’s communal approach to radiology, its relations with EU institutions, international organisations, and other stakeholders, as well as European projects and alliances.

Get in touch →

Membership
Back to main AI Blog

deep learning

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

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

Most used hashtags:

deep learningmachine learningmriradiomicsx-ray computed tomographyai newsCOVID-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyworkflowradiographyMDCT

Latest posts

AI applied to MRI reliably detects the presence of meniscus tears

European Radiology August 2024

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

Detection of femoropopliteal arterial steno-occlusion at MR angiography

European Radiology Experimental August 2024

This single-centre retrospective study aimed to evaluate a deep learning (DL) algorithm for detecting vessel steno-occlusions in peripheral arterial disease patients (PAD). The authors’ findings suggest that the proposed DL model is promising and an effective tool to assist in the detection of arterial steno-occlusions in PAD patients. Key points: Article: Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

European Radiology July 2024

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Shallow and deep learning classifiers in medical image analysis

European Radiology Experimental July 2024

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision

Read More →

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences MRA

Insights into Imaging July 2024

Cerebrovascular diseases are seen as a significant threat to human life and health, and the segmentation of brain blood vessels has become a scientific challenge. Therefore, the authors of this study aimed to develop a fully automated deep learning workflow capable of accurate 3D segmentation of cerebral blood vessels using convolutional neural networks (CNNs) and transformer models. The study, conducted

Read More →

Knee landmarks detection via deep learning

European Radiology June 2024

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

Read More →

Deep Learning-based framework shown as a valuable tool to determine the composition of thyroid nodules

European Radiology May 2024

The authors of this retrospective multicenter study proposed a deep learning-based framework to identify the composition of thyroid nodules while also assessing their malignancy risk. Their research demonstrated that convolutional neural networks (CNNs) were able to assist in the diagnosis of thyroid nodules and reduce the rate of unnecessary fine-needle aspiration. Key points: Article: Deep learning to assist composition classification

Read More →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

European Radiology May 2024

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

Read More →

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

Read More →

Label-set impact on deep learning-based prostate segmentation on MRI

Jakob Meglič January 2024

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

AI applied to MRI reliably detects the presence of meniscus tears

European Radiology August 2024

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

Detection of femoropopliteal arterial steno-occlusion at MR angiography

European Radiology Experimental August 2024

This single-centre retrospective study aimed to evaluate a deep learning (DL) algorithm for detecting vessel steno-occlusions in peripheral arterial disease patients (PAD). The authors’ findings suggest that the proposed DL model is promising and an effective tool to assist in the detection of arterial steno-occlusions in PAD patients. Key points: Article: Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

European Radiology July 2024

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Shallow and deep learning classifiers in medical image analysis

European Radiology Experimental July 2024

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision

Read More →

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences MRA

Insights into Imaging July 2024

Cerebrovascular diseases are seen as a significant threat to human life and health, and the segmentation of brain blood vessels has become a scientific challenge. Therefore, the authors of this study aimed to develop a fully automated deep learning workflow capable of accurate 3D segmentation of cerebral blood vessels using convolutional neural networks (CNNs) and transformer models. The study, conducted

Read More →

Knee landmarks detection via deep learning

European Radiology June 2024

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

Read More →

Deep Learning-based framework shown as a valuable tool to determine the composition of thyroid nodules

European Radiology May 2024

The authors of this retrospective multicenter study proposed a deep learning-based framework to identify the composition of thyroid nodules while also assessing their malignancy risk. Their research demonstrated that convolutional neural networks (CNNs) were able to assist in the diagnosis of thyroid nodules and reduce the rate of unnecessary fine-needle aspiration. Key points: Article: Deep learning to assist composition classification

Read More →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

European Radiology May 2024

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

Read More →

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

Read More →

Label-set impact on deep learning-based prostate segmentation on MRI

Jakob Meglič, Mohammed R. S. Sunoqrot, Tone Frost Bathen, Mattijs Elschot January 2024

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.