High performance data processing pipeline for connectome segmentation
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2016
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| Online Access: | http://hdl.handle.net/1721.1/106122 |
| _version_ | 1811077638467354624 |
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| author | Jakubiuk, Wiktor |
| author2 | Nir Shavit. |
| author_facet | Nir Shavit. Jakubiuk, Wiktor |
| author_sort | Jakubiuk, Wiktor |
| collection | MIT |
| description | Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2016. |
| first_indexed | 2024-09-23T10:46:12Z |
| format | Thesis |
| id | mit-1721.1/106122 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:46:12Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1061222019-04-12T17:09:25Z High performance data processing pipeline for connectome segmentation Jakubiuk, Wiktor Nir Shavit. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Wiktor Jakubiuk. M. Eng. in Computer Science and Engineering 2016-12-22T16:29:53Z 2016-12-22T16:29:53Z 2015 2016 Thesis http://hdl.handle.net/1721.1/106122 965799815 eng 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. http://dspace.mit.edu/handle/1721.1/7582 88 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Jakubiuk, Wiktor High performance data processing pipeline for connectome segmentation |
| title | High performance data processing pipeline for connectome segmentation |
| title_full | High performance data processing pipeline for connectome segmentation |
| title_fullStr | High performance data processing pipeline for connectome segmentation |
| title_full_unstemmed | High performance data processing pipeline for connectome segmentation |
| title_short | High performance data processing pipeline for connectome segmentation |
| title_sort | high performance data processing pipeline for connectome segmentation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/106122 |
| work_keys_str_mv | AT jakubiukwiktor highperformancedataprocessingpipelineforconnectomesegmentation |