# Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

arXiv: https://arxiv.org/abs/2112.02781 · HTML: https://mlsanity.com/paper/2112.02781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Multi-task Self-Supervised Visual Learning](https://mlsanity.com/paper/1708.07860/) (2017)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [Beyond the Pixel-Wise Loss for Topology-Aware Delineation](https://mlsanity.com/paper/1712.02190/) (2017)
- [Learning deep structured active contours end-to-end](https://mlsanity.com/paper/1803.06329/) (2018)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations](https://mlsanity.com/paper/1904.07934/) (2019)
- [DARNet: Deep Active Ray Network for Building Segmentation](https://mlsanity.com/paper/1905.05889/) (2019)
- [A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels](https://mlsanity.com/paper/1807.11719/) (2018)
- [Recurrent U-Net for Resource-Constrained Segmentation](https://mlsanity.com/paper/1906.04913/) (2019)
- [Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://mlsanity.com/paper/1907.11835/) (2019)
- [Simultaneous Edge Alignment and Learning](https://mlsanity.com/paper/1808.01992/) (2018)
- [Promoting Connectivity of Network-Like Structures by Enforcing Region Separation](https://mlsanity.com/paper/2009.07011/) (2020)
- [End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery](https://mlsanity.com/paper/2007.11691/) (2020)
- [ N4 -Fields: Neural Network Nearest Neighbor Fields for Image Transforms](https://mlsanity.com/paper/1406.6558/) (2014)
- [Joint Segmentation and Path Classification of Curvilinear Structures](https://mlsanity.com/paper/1905.03892/) (2019)
- [DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data](https://mlsanity.com/paper/2006.10802/) (2020)
- [Tracing in 2D to Reduce the Annotation Effort for 3D Deep Delineation](https://mlsanity.com/paper/1811.10508/) (2018)
- [Divergence Prior and Vessel-tree Reconstruction](https://mlsanity.com/paper/1811.09745/) (2018)

## Built on by (most cited first, 1 of 1)

- [Enabling Uncertainty Estimation in Iterative Neural Networks](https://mlsanity.com/paper/2403.16732/) (2024)

## Abstract (arXiv)

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet, in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. To this end, we treat the annotations as active contour models that can deform themselves while preserving their topology. This enables us to jointly train the network and correct potential errors in the original annotations. The result is an approach that boosts performance of deep networks trained with potentially inaccurate annotations. Code has been released at https://github.com/doruk-oner/AdjustingAnnotationswithSnakes.