The problem: Cell biologists in their effort to correctly identify individual cells, must use different chemicals on cell samples. There are 2 serious downsides to the current practice: the process itself is very costly and these chemicals end up destroying the cells used for identification. Using ML instead, to correctly identify these cells without the use of chemicals is the challenge. This project will use advance techniques to be able to recognize and identify cell boundaries and cells in a dense and packed cell environment with techniques that exceed current state of the art. Ximantis will benefit from such ML components which can then be used in computer vision for autonomous navigation and scene identification during live traffic to minimize accidents and risk.