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Lu,
Hang
Lu,
Hang
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ItemIDBR: an automated, high-throughput micro system for precision imaging and ablation of cells(Georgia Institute of Technology, 2011-07-29) Lu, Hang ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. School of Chemical and Biomolecular Engineering ; Georgia Institute of Technology. Office of Sponsored Programs
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ItemThe synthesis and structure-activity relationship study of azo dye related HIV replication inhibitors : Part 2: Plant isolation of signalling pathways inhibitors as anti-cancer agents(Georgia Institute of Technology, 1994-08) Lu, Hang ; Zalkow, Leon H. ; Chemistry and biochemistry ; Chemistry
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ItemProbing Worm Brain with Microfluidics and Light(Georgia Institute of Technology, 2011-05-18) Lu, Hang ; Georgia Institute of Technology. Microelectronics Research Center ; Georgia Institute of Technology. Nanotechnology Research Center ; Georgia Institute of Technology. School of Chemical and Biomolecular Engineering
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ItemTraining and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans(Georgia Institute of Technology, 2015) Zhan, Mei ; Crane, Matthew Muria ; Lu, Hang ; Ch'ng, QueeLim ; Entchev, Eugeni ; Caballero, Antonio ; de Abreu, Diana Andrea Fernandes ; Georgia Institute of Technology. School of Chemical and Biomolecular Engineering ; Georgia Institute of Technology. Interdisciplinary Bioengineering Graduate Program ; Kings College London. Institute of Psychiatry, Psychology, & Neuroscience. MRC Center for Developmental NeurobiologyWe demonstrate the utility of a generalizable classification routine for biological image processing by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. These data files contain all of the training and validation data used for the construction and validation of these classifiers.