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dc.contributor.advisorPrasad, Dilip
dc.contributor.advisorAgarwal, Krishna
dc.contributor.advisorBirgisdottir, Åsa Birna
dc.contributor.advisorAgarwal, Rohit
dc.contributor.authorCeleste, Aaron
dc.date.accessioned2023-06-02T14:27:09Z
dc.date.available2023-06-02T14:27:09Z
dc.date.issued2023-05-15
dc.description.abstractThe interactions between organelles within living cells have an effect on the health of the organism [1]. One way to study this behavior is to develop algorithms which can automatically detect and track objects in microscopy video data. A large amount of data is needed to train algorithms to identify particular behaviors in subcellular machinery. If the case of segmentation is considered, videos of these organelles are difficult to label because of the inability to locate objects with precision in microscopy videos. This thesis introduces CODS (Cell Organelle Dynamic Simulation), a solution for generating as much automatically labeled data as needed for use in several varieties of machine learning. CODS is able to work in parallel to produce dynamic complex simulations of mitochondria performing behaviors such as fission, fusion, and kiss-and-run in various behavior profiles. This lays the groundwork to easily build further complex interactions and to add organelle types to perform further development and study. CODS offers the ability to easily manipulate dozens of parameters relating to the type, number, and behavior of organelles desired. This includes the parameters of the microscope being simulated, providing the opportunity to produce data which works to mimic a variety of situations. I discuss the myriad uses of the CODS simulation engine and show that output from CODS is good enough to train a video segmentation algorithm to detect and track mitochondria through time in real microscopy data.en_US
dc.identifier.urihttps://hdl.handle.net/10037/29335
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDINF-3990
dc.subjectmitochondriaen_US
dc.subjectmachine learningen_US
dc.subjectbiologyen_US
dc.subjectvideoen_US
dc.subjectsegmentationen_US
dc.subjectfluorescenceen_US
dc.titlePresenting CODS (Cell Organelle Dynamic Simulation)en_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)