# Recurrent U-Net for Resource-Constrained Segmentation

arXiv: https://arxiv.org/abs/1906.04913 · HTML: https://mlsanity.com/paper/1906.04913/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 27 of 27)

- [Efficient Medical Image Segmentation Based on Knowledge Distillation](https://mlsanity.com/paper/2108.09987/) (2021)
- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2106.14033/) (2021)
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- [Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search](https://mlsanity.com/paper/2104.05309/) (2021)
- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [Learning With Context Feedback Loop for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.02844/) (2021)
- [Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices](https://mlsanity.com/paper/2206.02358/) (2022)
- [IT3: Idempotent Test-Time Training](https://mlsanity.com/paper/2410.04201/) (2024)
- [Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM](https://mlsanity.com/paper/2111.03615/) (2021)
- [Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints](https://mlsanity.com/paper/2107.01248/) (2021)
- [Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond](https://mlsanity.com/paper/2203.05709/) (2022)
- [JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI](https://mlsanity.com/paper/2210.12548/) (2022)
- [Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning](https://mlsanity.com/paper/2107.01361/) (2021)
- [Search for temporal cell segmentation robustness in phase-contrast microscopy videos](https://mlsanity.com/paper/2112.08817/) (2021)
- [Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation](https://mlsanity.com/paper/2208.06283/) (2022)
- [The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments](https://mlsanity.com/paper/2407.14649/) (2024)
- [Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate](https://mlsanity.com/paper/2112.02781/) (2021)
- [EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation](https://mlsanity.com/paper/2208.09197/) (2022)
- [RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement](https://mlsanity.com/paper/2309.07513/) (2023)
- [ItNet: iterative neural networks with small graphs for accurate, efficient and anytime semantic segmentation](https://mlsanity.com/paper/2101.08685/) (2021)
- [Object Based Attention Through Internal Gating](https://mlsanity.com/paper/2106.04540/) (2021)
- [PL-Net: Progressive Learning Network for Medical Image Segmentation](https://mlsanity.com/paper/2110.14484/) (2021)
- [IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules](https://mlsanity.com/paper/2406.12300/) (2024)
- [Deep Feedback Models](https://mlsanity.com/paper/2509.15905/) (2025)
- [Auto-Regressive U-Net for Full-Field Prediction of Shrinkage-Induced Damage in Concrete](https://mlsanity.com/paper/2509.20507/) (2025)
- [Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation](https://mlsanity.com/paper/2606.21061/) (2026)

## Abstract (arXiv)

State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on standard GPUs. In this paper, we introduce a novel recurrent U-Net architecture that preserves the compactness of the original U-Net, while substantially increasing its performance to the point where it outperforms the state of the art on several benchmarks. We will demonstrate its effectiveness for several tasks, including hand segmentation, retina vessel segmentation, and road segmentation. We also introduce a large-scale dataset for hand segmentation.