Development of a machine learning algorithm for the automatic analysis of microscopy images in an in-vitro diagnostic platform

In this thesis we present the development of machine learning algorithms for single cell analysis in an in-vitro diagnostic platform for Cellply, a startup that operates in precision medicine. We researched the state of the art of deep learning for biomedical image analysis, and we analyzed the impact that convolutional neural networks have had in object detection tasks. Then we compared neural networks that are currently used for cell detection, and we chose the one (i.e. Stardist) that is able to perform a more efficient detection also in a crowded cells context. We could train models using Stardist algorithm in the open-source platform ZeroCostDL4Mic, using code and GPU in Colab environment. We trained different models, intended for distinct applications, and we evaluated them using metrics such as precision and recall. These are our results: • a model for single channel brightfield images taken from samples of Covid patients, that guarantees a precision of about 0.98 and a recall of about 0.96 • a model for multi-channel images (i.e. a stack of multiple images, each one highlighting different contents) taken from experiments about natural killer cells, with precision and recall of about 0.81 • a model for multi-channel images taken from samples of AML (Acute Myeloid Leukemia) patients, with precision and recall of about 0.73 • a simpler model, trained to detect the main area (named "well") on which cells can be found, in order to discard what is out of this area. This model has a precision of about 1 and a recall of about 0.98. Finally, we wrote Python code in order to read a text input file that contains the necessary information to run a specified trained model for cell detection, with certain parameters, on a given set of images of a certain experiment. The output of the code is a .csv file where measurements related to every detected “object of interest” (i.e. cells or other particles) are stored. We also talk about future developments in this field.