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IntroductionThe goal of the Cytoseg project is to produce a tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. The project is written in Python and uses the completely open source pythonxy platform (which includes scipy and ITK image processing tools). Cytoseg is currently in beta stage. Cytoseg is part of the Cell Centered Database tool set. Example Output for Mitochondria Segmentation
MotivationModern electron microscopic staining and imaging technology can be used to highlight intracellular structures, such as vesicles and mitochondria, as well as cellular membranes resulting in complex, textured images. While staining of multiple structures makes it possible to accomplish the identification of most cellular and subcellular tissue components simultaneously, it makes automatic segmentation and identification of these more challenging. When addressing biological problems, automatic segmentation accuracy is critical, as each manual correction requires human effort and ultimately increases the time and cost required for segmentation. Modern three dimensional TEM, SEM, and SBFSEM images involve a large number of objects with various three dimensional shapes. Image intensity alone does not accurately identify a given structure, and identification of objects typically involves a knowledge of various textures and shapes present in the data. Therefore, the numerous segmentation algorithms developed for other biomedical imaging modalities are not directly applicable to thin sections from TEM and serial block face derived SEM images. Cytoseg uses machine learning at multiple steps in the process to address these challenges. InstallationUsageHow to view and edit results in IMOD AuthorPublicationsReportsAutomatic Detection and Segmentation of Mitochondria in 3D Electron Tomographic Images |
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