dc.contributor.advisor | Chung, Kwanghun | |
dc.contributor.author | Gu, Xinyi | |
dc.date.accessioned | 2022-02-07T15:10:52Z | |
dc.date.available | 2022-02-07T15:10:52Z | |
dc.date.issued | 2021-09 | |
dc.date.submitted | 2021-09-21T19:54:17.472Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/139887 | |
dc.description.abstract | Tissue-clearing methods, light-sheet microscopy, and antibody labeling enable extracting cellular and subcellular information, producing large amount of image data needs to be analyzed. Hundreds of heterogeneous cell types were detected through the data obtained across species and types of tissues. We developed a novel approach that is generally applicable to a wide range of cell types in the large-scale 3D brain datasets, using a pipeline that performs accurate detection of cells regardless of image resolution, labeling pattern, and tissue processing techniques used. The pipeline is compatible with various labeling techniques including IHC, Fluorescence in situ hybridization (FISH), and genetic labeling and can be used for cellular level quantification in all types of tissues. | |
dc.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | Copyright MIT | |
dc.rights.uri | http://rightsstatements.org/page/InC-EDU/1.0/ | |
dc.title | Generalist 3D Cell Phenotyping for All-Type Tissues | |
dc.type | Thesis | |
dc.description.degree | S.M. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Science in Electrical Engineering and Computer Science | |