There are many methods for obtaining microscopic images, they can be obtained for different types of cells in different environments, which makes it impossible to
create universal recognition algorithms. Therefore, for each specific task, it is necessary to implement a separate algorithm that will be effective for the existing set of images, and take into account the peculiarities of these images. The task of this work is the development of a flexible and customizable algorithm that can be configured to segment cells on different types of images. As a result, a solution was developed that has many customizable parameters to optimize the result for a specific data set. Also, this it is resistant to a lot of noise and artifacts that can occur on images, such as uneven background, small debris, loss of focus when shooting.