# Guiding 3D U-nets with signed distance fields for creating 3D models from images

arXiv: https://arxiv.org/abs/1908.10579 · HTML: https://mlsanity.com/paper/1908.10579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation](https://mlsanity.com/paper/2111.12996/) (2021)
- [NUDF: Neural Unsigned Distance Fields for high resolution 3D medical image segmentation](https://mlsanity.com/paper/2504.18344/) (2025)

## Abstract (arXiv)

Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.