High performance data processing pipeline for connectome segmentation
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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By investigating neural connections, neuroscientists try to understand the brain and reconstruct its connectome. Automated connectome reconstruction from high resolution electron miscroscopy is a challenging problem, as all neurons and synapses in a volume have to be detected. A mm3 of a high-resolution brain tissue takes roughly a petabyte of space that the state-of-the-art pipelines are unable to process to date. A high-performance, fully automated image processing pipeline is proposed. Using a combination of image processing and machine learning algorithms (convolutional neural networks and random forests), the pipeline constructs a 3-dimensional connectome from 2-dimensional cross-sections of a mammal's brain. The proposed system achieves a low error rate (comparable with the state-of-the-art) and is capable of processing volumes of 100's of gigabytes in size. The main contributions of this thesis are multiple algorithmic techniques for 2- dimensional pixel classification of varying accuracy and speed trade-off, as well as a fast object segmentation algorithm. The majority of the system is parallelized for multi-core machines, and with minor additional modification is expected to work in a distributed setting.
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2016."December 2015." Cataloged from PDF version of thesis.Includes bibliographical references (pages 83-88).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.