# Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation

arXiv: https://arxiv.org/abs/2406.17936 · HTML: https://mlsanity.com/paper/2406.17936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain](https://mlsanity.com/paper/1805.02718/) (2018)

## Built on by (most cited first, 1 of 1)

- [DaCapo: a modular deep learning framework for scalable 3D image segmentation](https://mlsanity.com/paper/2408.02834/) (2024)

## Abstract (arXiv)

Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).