Converting a Neuron-Morphology Reconstruction System: Open-Science Design and Implementation

The thesis describes the conversion of the Online Reconstruction and functional Imaging Of Neurons (ORION) system for neuron-morphology reconstruction from an interpreted language to a compiled language. The motivation of this conversion is to provide a tool that can be used by neuroscience researchers to analyze 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 organizations, it is possible to compare the 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 a result, much of the existing code needs to be analyzed to determine any changes needed 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) analyze 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 visualization; (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 reconstruction 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 that can also be extended by the community.