Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/122761 |
| _version_ | 1811091637945761792 |
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| author | Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology. |
| author2 | Nir Shavit. |
| author_facet | Nir Shavit. Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology. |
| author_sort | Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 |
| first_indexed | 2024-09-23T15:05:33Z |
| format | Thesis |
| id | mit-1721.1/122761 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:05:33Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1227612023-06-28T15:18:57Z Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology. 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: 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). 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. by Lu Mi. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-04T20:22:55Z 2019-11-04T20:22:55Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122761 1124925659 eng MIT 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. http://dspace.mit.edu/handle/1721.1/7582 44 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Mi, Lu(Electrical and computer science engineer)Massachusetts Institute of Technology. Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title | Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title_full | Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title_fullStr | Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title_full_unstemmed | Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title_short | Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics |
| title_sort | cross classification clustering multi object tracking technique for 3 d instance segmentation in connectomics |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/122761 |
| work_keys_str_mv | AT miluelectricalandcomputerscienceengineermassachusettsinstituteoftechnology crossclassificationclusteringmultiobjecttrackingtechniquefor3dinstancesegmentationinconnectomics |