Supervisor: Dmytro Fishman

Example of prediction by U-Net trained on Otsu on its best epoch (IOU = 0.7834). From top to bottom: original image, network prediction, ground truth.

Our goal

In this project we are exploring the potential of unsupervised approaches for semantic segmentation for nuclei segmentation task, as an alternative to fully supervised models that require large numbers of pixel-wise annotated data.


To measure the effect of a drug on a population of cells, one may be measuring the evolution of this population in a Petri dish, tracking cell locations, their number, and shape. Nuclei segmentation is one of the first steps in the analysis of microscopy images. In the past, nuclei segmentation was done manually; it is a tedious and time-consuming part of the pipeline…

Tetiana Rabiichuk

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