Segmentation of 3D microscopy images

In-vivo imaging provides a unique opportunity to examine complex cellular activity in live tissue. Images produced by these experiments are difficult to analyze manually, typically applied to mono-layer cell culture assays (i.e. cells in a dish). Recent advances in deep learning enable the opportunity to analyze these in-vivo tissue images with greater efficiency and accuracy. This project will apply deep learning based segmentation and classification technology to a dataset provided by a collaborating pharmaceutical company. Deep learning algorithms will be developed to segment different cell types and vascular structures in the dataset and quantify features (i.e. length, volume, protrusion number, marker intensity) of these objects. These features will be used to evaluate the effectiveness of therapeutic treatments.

Faculty Supervisor:

Sanja Fidler


Kshitij Gupta


Phenomic AI Inc


Computer science


Information and communications technologies




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