Network cycle features in relative neighborhood graphs

摘要

Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

主权项

1. A method for analyzing biomedical data comprising:
obtaining macroscopic imaging data; obtaining histopathological imaging data; generating a relative neighborhood graph from the histopathological imaging data by parallel computation; executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of an Urquhart-approximation of the relative neighborhood graph; quadtree-based partitioning of nuclei coordinates obtained from the histopathological imaging data across a plurality of processes; Morton ordering of the nuclei coordinates to optimize load balance; creating a shared boundary layer, wherein each of the processes Delaunay triangulates the shared boundary layer, and wherein remaining local points are added to each process's respective triangulations; pruning the triangulations to generate the relative neighborhood graph; registering the macroscopic imaging data and the histopathological imaging data; and correlating the macroscopic imaging data and the network cycle features.