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Dipartimento di Fisica - Politecnico di Milano

Cell tracking automatico per microscopia in vivo

 

Being able to track objects in a moving scene is a challenging task, required by a variety of applicative fields both in scientific and in industrial environments. In life-sciences, it is particularly important to track cells while they move in a living organism, in order to understand the mechanism of cells migration in many phenomena, including tissue development and regeneration. 

Optical microscopy, allows acquiring time-lapse volumetric imaging of cells and tissues at high spatio-temporal resolution. The student will contribute to setup a microscopy method, called Light Sheet Fluorescence Microscopy (LSFM), which is a fluorescence imaging technique well suited for rapid in-vivo imaging of multicellular organisms.

The aim of the thesis is the development of an acquisition and processing method, based on LSFM, which will be used to study living biological samples, including zebrafish, Arabidopsis Thaliana and Hydra Vulgaris. The method will give a better insight of their growth and regeneration processes.

 

 

 

 

 

 

 

Examples of biological samples acquired with LSFM: zebrafish embryo (left) and Arabidopsis thaliana (right). Single cells are visible in different colors. Their tracking will be the subject of the work.

The student will be initially trained on the use of a laser-based system (controlled in Labview) for data collection. Then, he/she will develop a code (in Python) specifically dedicated to dynamically recognize and track cells in the sample. This will be based on a computing tool that combines spatial discretization (or segmentation) with temporal evolution and machine learning techniques, so that cells will be automatically identified over a stack of (temporal) consecutive images. The method will also output information on the cell movement such as path, speed and distance covered.

Requirements:

Basic knowledge of at least one programming language (Java, C++, Python or Matlab): specific training will be given at the beginning of the thesis.

Good English, spoken and written.