# A Deep Learning based Fast Signed Distance Map Generation

arXiv: https://arxiv.org/abs/2005.12662 · HTML: https://mlsanity.com/paper/2005.12662/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 3 of 3)

- [Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks](https://mlsanity.com/paper/2104.12510/) (2021)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Bayesian Logistic Shape Model Inference: application to cochlea image segmentation](https://mlsanity.com/paper/2105.02045/) (2021)

## Abstract (arXiv)

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency.