Engineering Principles for Scalable Connectomics
Name
Garzon Navarro_monse_meche_sb_thesis_2025.pdf
Description
Thesis PDF
Size
43.16 MB
Format
Adobe PDF
Checksum (MD5)
17e684fa1fdd80b5e3d1f22a071acdf6
Author(s)
Garzon Navarro, Monserrate
Advisor(s)
Culpepper, Martin L.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Brain tissue sectioning presents a significant challenge in connectomics, particularly when scaling to larger volumes. In the MICrONS 1 mm³ mouse visual cortex dataset, 25.1% of scanned images—representing over a month of imaging work—were discarded due to sectioning defects. Current methods result in material loss during cutting and face limitations in tool wear and process efficiency. This thesis examines tissue sectioning through an engineering lens. Drawing from established machining practices and parallel industries, we propose and evaluate potential improvements to sectioning methods. The work aims to contribute to ongoing efforts in mapping larger connectomes, making it more practical and less error-prone.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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