GPU-Accelerated RSF Level Set Evolution for Large-Scale Microvascular
  Segmentation
Meher Niger Helya Goharbavang Taeyong Ahn Emily K. Alley Joshua D. Wythe Guoning Chen David Mayerich
Microvascular networks are challenging to model because these structures are
currently near the diffraction limit for most advanced three-dimensional
imaging modalities, including confocal and light sheet microscopy. This makes
semantic segmentation difficult, because individual components of these
networks fluctuate within the confines of individual pixels. Level set methods
are ideally suited to solve this problem by providing surface and topological
constraints on the resulting model, however these active contour techniques are
extremely time intensive and impractical for terabyte-scale images. We propose
a reformulation and implementation of the region-scalable fitting (RSF) level
set model that makes it amenable to three-dimensional evaluation using both
single-instruction multiple data (SIMD) and single-program multiple-data (SPMD)
parallel processing. This enables evaluation of the level set equation on
independent regions of the data set using graphics processing units (GPUs),
making large-scale segmentation of high-resolution networks practical and
inexpensive.
  We tested this 3D parallel RSF approach on multiple data sets acquired using
state-of-the-art imaging techniques to acquire microvascular data, including
micro-CT, light sheet fluorescence microscopy (LSFM) and milling microscopy. To
assess the performance and accuracy of the RSF model, we conducted a
Monte-Carlo-based validation technique to compare results to other segmentation
methods. We also provide a rigorous profiling to show the gains in processing
speed leveraging parallel hardware. This study showcases the practical
application of the RSF model, emphasizing its utility in the challenging domain
of segmenting large-scale high-topology network structures with a particular
focus on building microvascular models.