A trio of recent Waterloo graduates has tackled a long-standing problem in clinical research with an automated solution that could help scientists analyze blood stem cells faster and more accurately.
“During my co-op placement at Princess Margaret Cancer Centre I was working on a project that used immunofluorescence image analysis to answer a specific scientific question about hematopoietic stem cells,” said Isabella Di Biasio, a recent graduate of Waterloo’s biochemistry program. “I was having difficulties related to the ImageJ software researchers use. In talking to my supervisor, Dr. Stephanie Xie, I learned that many people at the cancer centre shared similar frustrations.”
“The process is incredibly manual,” Isabella explains. “Scientists use a software tool called ImageJ to classify cells, but they have to write their own scripts and analyze hundreds, even thousands of images by hand. It’s time consuming and prone to error.”
Recognizing the need for a solution, Isabella began to think how automation and machine learning might help, but as a biochemistry student she didn’t have the specific background in computer science.
That’s when she teamed up with Katarina Makivic and Veronika Sustrova, two recent graduates from Waterloo’s Computer Science program. The three students formed a team through the Interdisciplinary Capstone Design Course, an initiative that brings together students from across the university’s six faculties to tackle real-world challenges.
Read the full story from Computer Science to learn more.