New Geometric Deep Learning Model for Detecting Stroke Lesions

Ischemic stroke, which occurs when a blood vessel in the brain gets blocked by a clot, is among the leading causes of death worldwide. Fortunately, surgeons now have access to advanced imaging techniques that allow them to visualize the interior of a patient's brain during a stroke. This helps them pinpoint the location of the clot and analyze the extent of damage to the brain tissue.

Computed tomography-perfusion (CT-P) is one of the most useful imaging modalities in the early stages of an acute stroke. However, it is challenging to accurately identify segmentation - the outline of stroke lesions - in a CT-P scan, and the final diagnosis depends greatly on the surgeon's expertise and ability. To address this issue, scientists have come up with various machine learning models that perform automatic segmentation of CT-P scans. Unfortunately, none of them has reached a level of performance suitable for clinical applications.

Against this backdrop, a team of researchers from Germany recently developed a new segmentation algorithm for stroke lesions. As reported in their study published in the Journal of Medical Imaging, the team built a geometric deep learning model called "Graph Fully-Convolutional Network" (GFCN). The internal operations performed by their geometric algorithm differ fundamentally from those of the more widely used Euclidean models. In their study, the researchers explored the benefits and limitations of this alternative approach.

A key advantage of the proposed model is that it can better learn and preserve important features inherent to brain topology. By using a graph-based neural network, the algorithm can detect complex inter-pixel relationships from different angles. This, in turn, enables it to detect stroke lesions more accurately.

In addition, the team adopted “pooling” and “unpooling” blocks in their network structure. Put simply, the pooling operations, also called "downsampling," reduce the overall size of the feature maps extracted by the network from input images. This reduces the computational complexity of the algorithm, enabling the model to extract the most salient features of the CT-P scans. In contrast, the unpooling operations (or "upsampling") revert the pooling operations to help properly localize the detected features in the original image based on contextual cues. By combining these two operations, the network structure can extract richer geometric information.

The team conducted a series of analyses to determine the effect of each component of GFCN on its segmentation performance. They then compared the performance of the proposed algorithm against the state-of-the-art models, all trained using the same public dataset. Interestingly, although their model used basic unpooling techniques and a simple input configuration, it performed better than the conventional models under most conditions.

Notably, GFCN-8s, with three pooling layers and eight-fold upsampling, achieved a Dice coefficient score - a metric indicating the overlap between the predicted and actual lesion areas - of 0.4553, which is significantly higher than other models. Moreover, the proposed model could adapt to irregular segmentation boundaries better than the state-of-the-art models.

Overall, the findings of this study showcase the potential of geometric deep learning for segmentation problems in medical imaging. Further research on similar strategies could pave the way for highly accurate models for automatic stroke diagnosis that could improve patient outcomes and save lives.

Iporre-Rivas A, Saur D, Rohr K, Scheuermann G, Gillmann C.
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network.
J Med Imaging (Bellingham). 2023 Jul;10(4):044502. doi: 10.1117/1.JMI.10.4.044502

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...