Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics
Name
1124925659-MIT.pdf
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4.67 MB
Format
Adobe PDF
Checksum (MD5)
ac4f71ccd22f8dd5f3afa7ef510632d3
Author(s)
Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology.
Advisor(s)
Nir Shavit.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, cross-classification clustering (3C) is designed and implemented, it is a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. The 3C mechanism was applied to achieve state-of- the-art accuracy in connectomics - the nanoscale mapping of neural tissue from electron microscopy volumes. This reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. This algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 39-44).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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