Geometry-driven visualization of microscopic structures in biology

At a microscopic resolution, biological structures are composed of cells, red blood corpuscles (RBCs), cytoplasm and other microstructural components. There is a natural pattern in terms of distribution, arrangement and packing density of these components in biological organization. In this work, we propose to use N-point correlation functions to guide the analysis and exploration process in microscopic datasets. These functions provide useful feature spaces to aid segmentation and visualization tasks. We show 3D visualizations of mouse placenta tissue layers and mouse mammary ducts as well as 2D segmentation/tracking of clonal populations. Further confidence in our results stems from validation studies that were performed with manual ground-truth for segmentation.