High performance data processing pipeline for connectome segmentation

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Jakubiuk, Wiktor

Advisor dc:contributor.advisor

• Nir Shavit.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106122

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106122