mlsanityData to 14 Oct 2026

FastGeodis: Fast Generalised Geodesic Distance Transform

arXiv 2208.00001 ↗First posted 26 Jul 2022cs.CV eess.IVcited by 8

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Abstract (arXiv)

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

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

3 of 11 references.

References · 3

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  3. 2022ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentationcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 1.

8 papers.