End to End Trainable Active Contours via Differentiable Rendering
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Abstract (arXiv)
We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at https://github.com/shirgur/ACDRNet.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2019.
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Citing papers per year: 2021: 7; 2022: 2; 2023: 2; 2024: 1; 2025: 0; 2026: 3.
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- 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
- 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learningcited by 14
- 2021Contour Proposal Networks for Biomedical Instance Segmentationcited by 13
- 2022Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learningcited by 9
- 2021A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representationcited by 3
- 2022Deep Statistic Shape Model for Myocardium Segmentationcited by 3
- 2026CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRIcited by 3
- 2021Deep Active Contours Using Locally Controlled Distance Vector Flowcited by 2
- 2021ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Passcited by 1
- 2026Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervisioncited by 1
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Renderingcited by 1
- 2021BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotationcited by 0
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering2 Jul 2026
- 2026CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI13 May 2026
- 2026Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision24 Feb 2026
- 2024Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation9 Oct 2024
- 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning25 Sep 2023
- 2023Joint Dense-Point Representation for Contour-Aware Graph Segmentation21 Jun 2023
- 2022Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning5 Sep 2022
- 2022Deep Statistic Shape Model for Myocardium Segmentation21 Jul 2022
- 2021End-to-End Segmentation via Patch-wise Polygons Prediction5 Dec 2021
- 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec 2021
- 2021BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation21 Aug 2021
- 2021A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation18 Jun 2021
- 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
- 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learningcited by 14
- 2021Contour Proposal Networks for Biomedical Instance Segmentationcited by 13
- 2022Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learningcited by 9
- 2021A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representationcited by 3
- 2022Deep Statistic Shape Model for Myocardium Segmentationcited by 3
- 2026CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRIcited by 3
- 2021Deep Active Contours Using Locally Controlled Distance Vector Flowcited by 2
- 2021ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Passcited by 1
- 2026Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervisioncited by 1
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Renderingcited by 1
- 2021BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotationcited by 0
- 2021End-to-End Segmentation via Patch-wise Polygons Predictioncited by 0
12 most cited of 14 All 14 (JSON)
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering2 Jul 2026
- 2026CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI13 May 2026
- 2026Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision24 Feb 2026
- 2024Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation9 Oct 2024
- 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning25 Sep 2023
- 2023Joint Dense-Point Representation for Contour-Aware Graph Segmentation21 Jun 2023
- 2022Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning5 Sep 2022
- 2022Deep Statistic Shape Model for Myocardium Segmentation21 Jul 2022
- 2021End-to-End Segmentation via Patch-wise Polygons Prediction5 Dec 2021
- 2021BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation21 Aug 2021
- 2021A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation18 Jun 2021
- 2021ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass7 Jun 2021
12 newest of 14 All 14 (JSON)