Developed a Python wrapper library PySAASS for Semi-Automatic Augmented Statistical Segmentation (SAASS) which is a C++ library which allowed to follow a semi-automatic workflow to segment a series of images. Since the segmentation algorithm is computationally expensive, in order to optimize its speed the library was written in C++ however that made its API not very intuitive and tedious to work with. Python wrapper solved this since it allowed for much simpler user-interface which in turn allowed to test the library in different environments much faster which was beneficial in a research environment.

Worked on developing a script for making probability distribution cloud of where anatomical structures are likely to be located within a human body. This cloud, when combined with various segmentation methods (e.g. SAASS), can result in improved segmentation accuracy as well as better performance since the cloud is derived from previously segmented structures segmented by medical staff.

Developed web application/interface for a system that allows medical professionals to digitally segment physical structures (organs) used in cancer treatment - Semi-Automatic Augmented Statistical Segmentation (SAASS). It was a full-stack development, which included the back-end design as well as the user-interface. The back-end was done primarily in Python using Django web-development-framework and the front-end was developed using HTML5, CSS3, and JS.