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dc.contributor.advisorEdward S. Boyden.en_US
dc.contributor.authorWohlwend, Jeremyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-02-08T15:58:16Z
dc.date.available2018-02-08T15:58:16Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/113451
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 49-51).en_US
dc.description.abstractDecades of work in computational power and high resolution imaging have made it possible to observe specific neurons and their connections at a microscopic level. This level of resolution is known as the connectomic level. While we have been able to observe the brain at such resolution for some time thanks to electron microscopy (EM), the analysis of the resulting data has proven to be a very challenging task, mainly because it relies only on membrane contrast to differentiate cells. Expansion microscopy (ExM) offers an exciting alternative to EM by expanding physical brain tissue, with the added benefits that regular optical microscopes can be used for image acquisition. This will enable a faster, cheaper, and multicolor approach to connectomics, and may have the potential to realistically tackle a nervous system as large and complex as a mammalian brain. Currently, the study and the development of Expansion Microscopy are limited by the rate at which experiments run, which can take weeks, and sometimes months. In a previous project, we described a pipeline to create synthetic ExM images (SimExM) and discussed the importance of ground truth data in experimenting with segmentation algorithms. One of the main bottlenecks in creating a full connectome is the need for these ground truth annotations which require a substantial amount of manual work. This work explores various reconstruction strategies based on ExM. We evaluate their dependency on manual annotations and their overall performance, and contribute, as such, to the development of a scalable connectomics pipeline.en_US
dc.description.statementofresponsibilityby Jeremy Wohlwend.en_US
dc.format.extent51 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleEnabling scalable multicolor connectomics through expansion microscopyen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1020179102en_US


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