# Deep Active Contours

arXiv: https://arxiv.org/abs/1607.05074 · HTML: https://mlsanity.com/paper/1607.05074/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2016.

## Built on by (most cited first, 9 of 9)

- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation](https://mlsanity.com/paper/2203.13312/) (2022)
- [A Deep Active Contour Model for Delineating Glacier Calving Fronts](https://mlsanity.com/paper/2307.03461/) (2023)
- [Cell Instance Segmentation: The Devil Is in the Boundaries](https://mlsanity.com/paper/2510.09848/) (2025)
- [Deep Active Contours Using Locally Controlled Distance Vector Flow](https://mlsanity.com/paper/2105.08447/) (2021)
- [End-to-End Segmentation via Patch-wise Polygons Prediction](https://mlsanity.com/paper/2112.02535/) (2021)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging](https://mlsanity.com/paper/2203.03336/) (2022)
- [Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation](https://mlsanity.com/paper/2410.07421/) (2024)

## Abstract (arXiv)

We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.