AI Frame of Mind: Neural Networks Bring Speed, Consistency to Brain Scan Analysis
In the field of neuroimaging, two heads are better than one. So radiologists around the globe are exploring the use of AI tools to share their heavy workloads — and improve the consistency, speed and accuracy of brain scan analysis.
“We often refer to manual annotation as the gold standard for neuroimaging, when it’s actually probably not,” said Tim Wang, director of operations at the Sydney Neuroimaging Analysis Centre, or SNAC. “In many cases, AI provides a more consistent, less biased evaluation than manual classification or segmentation.”
An Australian company co-located with the University of Sydney’s Brain and Mind Centre, SNAC conducts neuroimaging research as well as commercial image analysis for clinical research trials. The center is building AI tools to automate laborious analysis tasks in their research workflow, like 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.
SNAC uses the NVIDIA DGX-1 and DGX Station, powered by NVIDIA V100 Tensor Core GPUs, as well as PC workstations with NVIDIA GeForce RTX 2080 Ti graphics cards. The researchers develop their algorithms using the NVIDIA Clara suite of medical imaging tools, as well as cuDNN libraries and TensorRT inference software.
Brainstorming AI Solutions
When developing medicines, pharmaceutical companies conduct clinical trials to test how effective a new drug treatment is — often using brain imaging metrics such as brain atrophy rates and lesion changes as key indicators.
To ensure accurate and consistent measurements, pharma companies rely on centralized reading centers that evaluate trial participants’ brain scans in a blind analysis.
That’s where SNAC comes in. It analyzes patient MRI and CT scans acquired at clinical sites around the world. Its expertise in multicenter studies makes it well-positioned to develop AI tools that address challenges faced by radiologists and clinicians.
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 center’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.
“That’s no small difference,” Wang said. “Previously, this would take our analysts 20 to 30 minutes with semi-automatic methods. Now, that’s down to 2 or 3 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 3 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.
“By providing quantitative, individualized neuroimaging measurements, we can help streamline and add value to clinical radiology,” said Wang.
The center 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’s 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.
“Someone can develop a fantastic tool, but it’s hard to ask radiologists to use it by opening yet another application, or another browser on their workstations,” Wang said. “They don’t want to do that simply because they’re time poor, often punching through a very large volume of clinical scans a day.”
Main image shows 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 Sydney Neuroimaging Analysis Center.
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