Australia’s SNAC develops AI tools to improve brain scan analysis

SNAC plans to integrate its analysis tools with systems already used by clinicians, so that once a scan is taken, it is automatically routed to a server and processed.
By Dean Koh
03:17 AM

Above: A side-by-side comparison of multiple sclerosis lesion segmentation. Left image shows manual lesion segmentation, while right shows fully automated lesion segmentation. Image courtesy of SNAC.

Sydney Neuroimaging Analysis Centre (SNAC), an Australian company co-located with the University of Sydney’s Brain and Mind Centre, is building AI tools to automate laborious analysis tasks in their research workflow, such as isolating brain images from head scans and segmenting brain lesions.

Additional algorithms are in development and being validated for clinical use. One compares how a patient’s brain volume and lesions change over time. Another flags critical brain scans, so radiologists can more quickly attend to urgent cases. The researchers develop their algorithms using the NVIDIA Clara suite of medical imaging tools, as well as cuDNN libraries and TensorRT inference software.


SNAC analyses patient MRI and CT scans acquired at clinical sites around the world. With a training dataset of more than 15,000 three-dimensional CT and MRI images, SNAC is building its deep learning algorithms using the PyTorch and TensorFlow frameworks.

One of the centre’s AI models automates the time-consuming task of cleaning up MRI images to isolate the brain from other parts of the head, such as the venous sinuses and fluid-filled compartments around the brain. Using the NVIDIA DGX-1 system for inference, SNAC can speed up this process by at least 10x. Using semi-automatic methods, the process would take SNAC’s analysts 20-30 minutes but now it can be reduced to two or three minutes of pure machine time, while performing better and more consistently than a human.

Another tool tackles brain lesion analysis for multiple sclerosis cases. In research and clinical trials, image analysts typically segment brain lesions and determine their volume by manually examining scans — a process that takes up to 15 minutes. AI can shrink the time needed to determine lesion volume to just three seconds. That makes it possible for these metrics to be used in clinical practice as well, where due to time constraints, radiologists often simply eyeball scans to estimate lesion volumes.


The centre collaborates with I-MED, one of the largest imaging providers in the world, as well as the computational neuroscience team at the University of Sydney’s Brain and Mind Centre. The group also works closely with radiologists at major Australian hospitals to validate its algorithms. 

SNAC plans to integrate its analysis tools with systems already used by clinicians, so that once a scan is taken, it is automatically routed to a server and processed. The AI-evaluated scan is then passed on to radiologists’ viewers — giving them the analysis results without altering their workflow.


Also located in Sydney, Macquarie University and Macquarie Medical Imaging has recently partnered with GE Healthcare and Fujitsu Australia to research the ways artificial intelligence can help diagnose and monitor brain aneurysms on scans faster and more efficiently. The university will provide clinical expertise for the development and testing of the technology, which is provided by GE Healthcare, while Fujitsu will lead the initiative.

Singapore’s National Neuroscience Institute (NNI) is working with local medtech company specialising in AI and surgical robotics, Iota MedTech to develop a system to sort brain scans of head injury patients in order of urgency, Healthcare IT News reported in May this year. The development of the priority sorting system will help ensure that patients requiring immediate medical attention receive the care they need.


“We often refer to manual annotation as the gold standard for neuroimaging, when it’s actually probably not,” said Tim Wang, director of operations, SNAC in a statement. “In many cases, AI provides a more consistent, less biased evaluation than manual classification or segmentation.”

Your subscription has been saved.
Something went wrong. Please try again.