FastGeodis: Fast Generalised Geodesic Distance Transform

TL;DR

FastGeodis offers a highly efficient CPU and GPU implementation for computing geodesic and Euclidean distance transforms, significantly outperforming previous non-parallel implementations in 2D and 3D data processing.

Contribution

It introduces a parallelisable raster scan method implementation for distance transforms, achieving up to 74x speedup on GPU and 20x on CPU over prior non-parallel methods.

Findings

Up to 20x speedup on CPU

Up to 74x speedup on GPU

Supports 2D and 3D data

Abstract

The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware. In particular, it implements the paralellisable raster scan method from Criminisi et al. (2009), where elements in a row (2D) or plane (3D) can be computed with parallel threads. This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library (Wang, 2020) that uses a non-parallelisable single-thread CPU implementation. The performance speedups reported here were evaluated using 3D volume data on an Nvidia GeForce Titan X (12 GB) with a 6-Core Intel Xeon E5-1650 CPU. Further in-depth comparison of performance improvements are discussed in the FastGeodis documentation: https://fastgeodis.readthedocs.io