# FastGeodis: Fast Generalised Geodesic Distance Transform

arXiv: https://arxiv.org/abs/2208.00001 · HTML: https://mlsanity.com/paper/2208.00001/

## 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 (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation](https://mlsanity.com/paper/2201.04584/) (2022)

## Built on by (most cited first, 8 of 8)

- [TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model](https://mlsanity.com/paper/2412.06011/) (2024)
- [HiMo: High-Speed Objects Motion Compensation in Point Clouds](https://mlsanity.com/paper/2503.00803/) (2025)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)
- [SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression](https://mlsanity.com/paper/2405.16813/) (2024)
- [Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation](https://mlsanity.com/paper/2303.13696/) (2023)
- [Leveraging point annotations in segmentation learning with boundary loss](https://mlsanity.com/paper/2311.03537/) (2023)
- [Scribble-Based Interactive Segmentation of Medical Hyperspectral Images](https://mlsanity.com/paper/2408.02708/) (2024)
- [SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction](https://mlsanity.com/paper/2609.24864/) (2026)

## 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