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The goal of the Cell Organelle Segmentation in Electron Microscopy project team is to develop tools for the automated identification of all intracellular substructures within isotropic electron microscopy data.
With the increasing availability of large-volume, high-resolution datasets made possible via Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) there is also an expanding need to extract the multitude of information stored within these data-rich volumes.
In order to analyze these data, segmentation is first required. However, a purely manual segmentation is too time-consuming for large volumes. Thus, it is unfeasible to segment every substructure within an entire FIB-SEM volume, and an opportunity is missed.
We are developing tools for automated identification of all intracellular substructures within isotropic FIB-SEM data. We trained a deep neural network to directly and simultaneously predict signed boundary distances to the nearest object boundary of 36 classes of cellular substructures. Naive thresholding of these predictions at zero produces a promising initial segmentation of cellular substructures, while different thresholds form a component tree. As a convenient side effect, boundary distance predictions also allow to immediately extract the distance between object instances, providing organelle-organelle contact sites for free. This project provides a platform for the automated segmentation and analysis of numerous FIB-SEM datasets across many cells types within different tissues and from different species.