In Partial Fulfillment of the Requirements for the Degree of Master of Science

Zakariyya Mughal

will defend his thesis

Converting a Neuron Morphology Extraction System for Open Science: Design and Implementation

Abstract

The thesis describes the conversion of the ORION system for neuron morphology extraction from an interpreted language to a compiled language. The purpose of this conversion is to provide a tool that can be used by neuroscience researchers to analyse their own neuron data and compare the output against both manual and automated tracings. This is in line with the goals of open science: a movement that seeks to make the findings and processes of research more widely available for peer review and reproducibility. By collaboratively sharing both neuron imaging data and code between organisations, it is possible to compare results of multiple methods without reimplementing all the stages of the reconstruction pipeline.

In order to release the existing algorithm so that it can easily be incorporated into other tools, the implementation must be rewritten in a different language. This presents a challenge because the languages have vastly different paradigms. As such, much of the existing code needs to be analysed to determine any changes to the design. Creating a new implementation also means that the new system can be designed with modifiability in mind so that future changes can be easily incorporated. The specific objectives are to (i) analyse the ORION algorithm and implementation to determine the architecture for the new system that is efficient and extensible; (ii) integrate the system into a popular toolkit for biomedical image analysis for ease-of-use and visualisation; (iii) develop a test suite of both the individual components (unit testing) and across the whole system (integration tests); and (iv) ensure that the software gives reproducible results by making it easy to build and distribute.

The extraction of neuron morphology from microscopy imaging data is an invaluable method for understanding neuron characteristics. However, due to the cost in time and effort, manual neuron reconstruction is not feasible for large-scale analysis of neuron datasets. This implementation provides a working method for determining neuron morphology that can be used to collect statistical properties from various neuron data.

Date: Monday, November 30, 2015
Time: 12:00 PM
Place: HBS 317
Advisor: Dr. Ioannis A. Kakadiaris

Faculty, students, and the general public are invited.