# End-to-End Deep Convolutional Active Contours for Image Segmentation

arXiv: https://arxiv.org/abs/1909.13359 · HTML: https://mlsanity.com/paper/1909.13359/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [Deep Active Contours Using Locally Controlled Distance Vector Flow](https://mlsanity.com/paper/2105.08447/) (2021)
- [Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators](https://mlsanity.com/paper/2108.08891/) (2021)
- [Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks](https://mlsanity.com/paper/2307.07344/) (2023)
- [Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation](https://mlsanity.com/paper/2410.07421/) (2024)

## Abstract (arXiv)

The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields. Incorrectly, however, the ACM's differential-equation-based formulation and prototypical dependence on user initialization have been regarded as being largely incompatible with the recently popular deep learning approaches to image segmentation. This paper introduces the first tight unification of these two paradigms. In particular, we devise Deep Convolutional Active Contours (DCAC), a truly end-to-end trainable image segmentation framework comprising a Convolutional Neural Network (CNN) and an ACM with learnable parameters. The ACM's Eulerian energy functional includes per-pixel parameter maps predicted by the backbone CNN, which also initializes the ACM. Importantly, both the CNN and ACM components are fully implemented in TensorFlow, and the entire DCAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. As a challenging test case, we tackle the problem of building instance segmentation in aerial images and evaluate DCAC on two publicly available datasets, Vaihingen and Bing Huts. Our reseults demonstrate that, for building segmentation, the DCAC establishes a new state-of-the-art performance by a wide margin.