Founded in 1871, The Alfred is the oldest Melbourne hospital built in honour of His Royal
Highness, Prince Alfred, Duke of Edinburgh, who survived an assassination
attempt in 1969 while visiting Australia. Alfred’s medical imaging department
services two hospitals; Alfred health and Sandringham hospital. One of their
visions is to develop and use technology to improve diagnostic imaging and
reporting with Artificial Intelligence and Deep Learning.
Being the best imaging department in the country, they have received multiple accreditation awards
including Full DIAS (Diagnostic Imaging Accreditation Scheme) compliance. These awards
reflect their community of diligent and dynamic staff, who are devoted to
caring for patients’ needs. The services they provide include X RAY,
CT scan, Dexa Scan, Mammography, Ultrasound, MRI, Fluoroscopy, and Angiography.
Alfred's medical imaging department reached out to DiGiCOR. The situation
required a server that could store both large amounts of up to 100TB of
image data from Alfred Health and Sandringham Hospital as well
as compute. Ultimately, they needed this system so that
it could be used for Artificial Intelligence and Deep learning.
The overall solution consisted of a scalable GPU server with support for up to 8x GPUs and direct attached high-performance storage via 44 bay disc expansion shelf.
Greater flexibility via segmented GPU servers, allowing the solution to be scalable.
Provided a space of up to 100TB of storage via direct-attached storage.
Obtain off-the-cost reliable hardware that wouldn’t require frequent maintenance
Require large-scale storage of up to 100TB to store hundreds and thousands of CT scans for Artificial Intelligence and Deep Learning
Have a system that was expandable of up to eight GPUs
HOW WE HELPED
DiGiCOR provided a scalable and expandable GPU-optimised system to accommodate future growth
Provided a system that had both storage and compute located on the same system to enable Artificial Intelligence and Deep Learning use case
Connected two physically distant imaging departments in two hospitals via a scalable AI stack
Supplied NVIDIA Quadro RTX 8000 GPUs that give significant memory for three-dimensional CT scan
In Need of a Reliable and Flexible System
The medical imaging department for Alfred Health and Sandringham Hospital wanted off-the-cost
reliable hardware that wouldn’t require frequent maintenance. Previously, they
were extracting and storing data on clinical systems. They found that this
was expensive, limited the amount of data they could store, and made it
difficult to share information with other researchers. To create flexibility
to share data with other researchers, they needed a separate hardware partitioned from the clinical system.
Furthermore, because one of their focal points was creating storage for the research units,
they required large-scale storage of up to 100TB to store hundreds and thousands of CT scans
for Artificial Intelligence and Deep Learning. This was crucial as they needed Artificial
Intelligence and Deep Learning to learn from these CT scans.
This included Ubuntu stack and XNAT; which is a research PACS that stores images and organises them in a library.
Their other requirements consisted of having a system that was expandable of up to eight GPUs.
Due to the current requirements, two or four slots would be immediately occupied and the
remaining slots would allow simple expansion. In addition, because GPU technology
is rapidly growing, they wanted to be able to upgrade when a new NVIDIA was
released either having to be stuck with legacy hardware or having to buy a
new system. This meant that scalability was another important factor.
How We Helped
DiGiCOR provided a Supermicro server that was GPU optimised with up to eight GPU slots. This gave them
the scalability and expandability requirements they needed. It is also a system, designed for
high-performance computing for 3D imagery which made it suitable for medical imaging. The
entire solution stack is enterprise-grade with best-in-breed componentry.
DiGiCOR also provided NVIDIA Quadro RTX 8000 graphics card because their use case required
significant amounts of GPU memory as CT scans are three-dimensional. Training time was
not a critical requirement so the NVIDIA Quadro RTX 8000 was selected due to its
large memory capacity and cost-effectiveness.
The overall solution consisted of a scalable GPU server with support for up to
8x GPUs and direct attached high-performance storage via 44 bay disc expansion shelf.
As the GPU server and storage were segmented into separate elements, greater
flexibility and expandability were achieved. We also provided a system that was
scalable to enable them to upgrade to newer GPUs, and expandable to allow them to use up to eight graphics cards.
In addition, we also provided the medical imaging department with significant space
of up to 100TB of storage via direct attached storage for medical imagery to be
used for Artificial Intelligence and Deep Learning for CNN, Image recognition, detection and classification.
Currently, they have populated half the GPU unit and 80%
drive base. They were also able to meet their objectives and goals with this
solution because they were able to store legacy CT and medical imaging on the system.
Moreover, they were also able to do server-reach projects
with this server to complete research projects. The medical department for Alfred
Health and Sandringham hospital continues to be the best imaging department in the country
as a result of using technology to improve diagnostic imaging and reporting.