Contextual segmentation of large, high-dimensional medical images

State-of-the-art serial block face scanning electron microscopy (SBF-SEM) is used in cellular research to capture large 2D images of sliced tissue. These 2D images collectively form a 3D digital representation of the tissue. SBF-SEM was recently used to reconstruct the first 3D ultrastructural analysis of neural, glial, and vascular elements that interconnect to form the neurovascular unit (NVU) in the retina. Identification of relevant cell morphologies enables the examination of heterocellular interactions which aid our understanding of the structure and function of key retinal cells in diseased and healthy states. Disruption of the retinal NVU is thought to underlie the development of several retinal diseases​. However, the exact way in which the morphology of the retinal NVU is disrupted at nanoscale has yet to be clarified in 3D due to its structural complexity. Analysis of these images requires the annotation of relevant structures which is currently performed manually and takes several months to complete for a single tissue sample. This work explores a novel approach to automatically annotate these structures to accelerate current investigations and provide opportunities for future studies.