Data Driven Method for Building 3d Anatomical Models

Abstract

We present a method for building 3d models that takes into account the presence of tissue sampling. The purpose of this method is twofold: (1) Researchers who reconstruct 3D models will be able to calculate and understand how close the resulting model is from the “ideal” or actual structure, and (2) it enables the calculation of a reconstruction project’s cost in terms of time, effort, digital storage, computation, and money. The data-driven approach uses a single fully-sectioned tissue block as pilot data, builds a 3D model, and then derives a set of metrics related to the objects under biological investigation. In our work, we are interested in the 3D reconstruction of microvessel architecture in the trigone region between the vagina and the bladder as a potential avenue for drug delivery to treat bladder infection. In our application, by measuring the sizes of the structures of interest and calibrating the modeling accordingly, it is possible to (1) reduce the total overall cost of a large project by collecting more patient samples for a given budget, or (2) collect a richer dataset by only using 25% of the sliced tissue for structural modeling while leaving the rest for multimodal imaging or genomic processing.