# Deep Active Contours Using Locally Controlled Distance Vector Flow

arXiv: https://arxiv.org/abs/2105.08447 · HTML: https://mlsanity.com/paper/2105.08447/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 10 of 10)

- [Learning deep structured active contours end-to-end](https://mlsanity.com/paper/1803.06329/) (2018)
- [DARNet: Deep Active Ray Network for Building Segmentation](https://mlsanity.com/paper/1905.05889/) (2019)
- [Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation](https://mlsanity.com/paper/1704.03593/) (2017)
- [End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery](https://mlsanity.com/paper/2007.11691/) (2020)
- [End to End Trainable Active Contours via Differentiable Rendering](https://mlsanity.com/paper/1912.00367/) (2019)
- [Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network](https://mlsanity.com/paper/1809.01687/) (2018)
- [Deep Active Contours](https://mlsanity.com/paper/1607.05074/) (2016)
- [Adversarial Deep Structured Nets for Mass Segmentation from Mammograms](https://mlsanity.com/paper/1710.09288/) (2017)
- [End-to-End Deep Convolutional Active Contours for Image Segmentation](https://mlsanity.com/paper/1909.13359/) (2019)
- [Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net](https://mlsanity.com/paper/1808.08885/) (2018)

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

- [ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass](https://mlsanity.com/paper/2106.03382/) (2021)
- [A Segmentation Method for fluorescence images without a machine learning approach](https://mlsanity.com/paper/2212.13945/) (2022)

## Abstract (arXiv)

Active contours Model (ACM) has been extensively used in computer vision and image processing. In recent studies, Convolutional Neural Networks (CNNs) have been combined with active contours replacing the user in the process of contour evolution and image segmentation to eliminate limitations associated with ACM's dependence on parameters of the energy functional and initialization. However, prior works did not aim for automatic initialization which is addressed here. In addition to manual initialization, current methods are highly sensitive to initial location and fail to delineate borders accurately. We propose a fully automatic image segmentation method to address problems of manual initialization, insufficient capture range, and poor convergence to boundaries, in addition to the problem of assignment of energy functional parameters. We train two CNNs, which predict active contour weighting parameters and generate a ground truth mask to extract Distance Transform (DT) and an initialization circle. Distance transform is used to form a vector field pointing from each pixel of the image towards the closest point on the boundary, the size of which is equal to the Euclidean distance map. We evaluate our method on four publicly available datasets including two building instance segmentation datasets, Vaihingen and Bing huts, and two mammography image datasets, INBreast and DDSM-BCRP. Our approach outperforms latest research by 0.59 ans 2.39 percent in mean Intersection-over-Union (mIoU), 7.38 and 8.62 percent in Boundary F-score (BoundF) for Vaihingen and Bing huts datasets, respectively. Dice similarity coefficient for the INBreast and DDSM-BCRP datasets is 94.23% and 90.89%, respectively indicating our method is comparable to state-of-the-art frameworks.