As the final project for this Master’s thesis, I have developed a parallel 3D clustering algorithm to perform the spatiotemporal segmentation of the active brain cell regions in a series of black and white images recording a signal of interest. Its Python implementation—also boosted by a library written in C—has proven to be reasonably fast and to scale well according to both the strong and the weak scaling paradigms.
I realized the present work while working as a postdoctoral researcher in the Center for Neuroscience and Cognitive Systems of the Italian Institute of Technology (IIT), Rovereto (Italy) lead by Dr. Stefano Panzeri. The raw data has been collected at the Optical Approaches to Brain Function Lab lead by Dr. Tommaso Fellin at the IIT headquarters, Genoa (Italy). In compliance to a nondisclosure agreement between these two labs and the Scientific Board of the SISSA-ICTP Master in High-Performance Computing, no details about either the raw data or their processing methods will be unveiled in this thesis, with the exception of a series of computational benchmarks of the developed clustering algorithm and a general overview about its implementation, collected in Chapter 1. This policy has been adopted in order to preserve the confidential status of the ongoing project this work is part of.