MEng students use AI to improve imaging tool used during breast cancer surgery

Two standing figures on either side of a monitor, one pointing at an image
Bryant Bak-Yin Lim (BME MEng candidate, left) and Ali Yassine (ECE MEng candidate, right) simulate reviewing a breast cancer tissue scan. As interns at Perimeter Medical Imaging, Lim and Yassine developed new AI algorithms for breast cancer imaging. (Photo: Neil Ta)

AUGUST 3, 2023 • By Matthew Tierney

Last summer, Bryant Bak-Yin Lim (BME MEng candidate) and Ali Yassine (ECE MEng candidate) got a chance to make a difference in the lives of patients by improving how breast cancer surgery is performed.

The two graduate students were on MITACS internships at Perimeter Medical Imaging, a company with offices in Dallas, Texas and Toronto. As interns, they developed AI algorithms for the next generation of an FDA-cleared medical imaging system that helps surgeons visualize tissue microstructures during a lumpectomy to determine whether they have excised all the cancerous tissue.

Their algorithms prioritize suspicious images, making it easier for surgeons to parse the images and reduce time spent in the operating room (OR).

The Perimeter imaging device is about the size and shape of a small photocopier, says Lim, and is situated within the operating area. It employs a technology called optical coherence tomography (OCT), which is similar to ultrasound technology but uses light instead of sound to generate images, resulting in an image resolution ten times greater than ultrasound.

OCT has been widely used in clinical settings, including ophthalmology, dermatology and interventional cardiology, but Perimeter’s device is the first to bring wide-field OCT imaging into the OR.

“The tissue removed from the patient is put in a plastic bag and placed on a glass imaging plate on the device, using mild suction to hold it in place,” Lim says. “Light shoots up from the optical imaging system below, penetrates the tissue and reflects back into the device, which then displays results as a digital image on the monitor.”

Surgeons are looking for any suspicious features in what’s called the ‘margin,’ striving for about a two-millimetre rim of healthy tissue along the outer edges of the excised tissue.

“Currently, to assess a margin, specimens are sent out to a pathologist. That process usually takes days,” says Yassine. “If there’s cancerous tissue left, patients sometimes have to go back for another procedure, with all the risks and resource costs that come with it.

“The type of deep learning algorithm that I trained, called a convolutional neural network, can analyze the tissue image and identify whether the material is suspicious or non-suspicious with a very high accuracy rate.”

The challenge then is to display this analysis for the surgeon so they can make a timely, informed decision on whether they need to return to the operating table and remove more tissue from the patient, who is still under anesthesia.

Lim, whose MEng was collaborative with his medical degree, was tasked with building an efficient user interface to guide the surgeon.

“This device typically outputs hundreds of images, and it’s challenging for a surgeon in the OR to read through all of them and make a decision on the spot,” he says.

“I developed an algorithm that clustered images together based on certain parameters and then displayed only the most representative one.”

The algorithm reduced the hundreds of images to a more manageable number of thumbnails that account for all the information gathered from the tissue scan. The surgeon can also manipulate the digital images to see the tissue from different perspectives.

There is great potential for AI-enhanced tools to make the medical professional’s work — and patient’s experience — smoother, says ECE professor Ervin Sejdić, who supervised both students.

“The Perimeter device that Bryant and Ali worked on is part of a wave of new tools that do the grunt work of sorting through and repackaging the copious amounts of data necessary for complex procedures or diagnoses,” says Sejdić.

“This helps doctors sharpen their focus on the treatment.”

For his part, Yassine didn’t expect he would be this interested in medicine before he undertook this internship. He is finishing up his MEng project — a multiclass labeller algorithm for Perimeter that identifies specific tissues in breast cancer samples — and is planning to continue his career in med tech.

“I had my own personal health challenges a while back, and that has motivated me to work in this field,” he says. “It’s nice to help people through technology.”

Lim, who has two years left to complete his medical degree, says, “I hope to combine parts of AI and medicine and apply that to my future practice, whether industry research or some other collaborations. That’s where I want to bring my career to.”

“We are growing our MEng program in part because there are so many exciting possibilities out there for graduates,” says Chair of ECE Professor Deepa Kundur.

“Lim and Yassine’s internships at Perimeter demonstrate how quickly hands-on training can translate into real-world results.”

Note: Technologies referenced are currently not available for sale in the United States and have not been evaluated by the FDA.

For more information:

Jessica MacInnis
External Relations Manager
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering
416-978-7997 | jessica.macinnis@utoronto.ca