# Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation

arXiv: https://arxiv.org/abs/2105.14732 · HTML: https://mlsanity.com/paper/2105.14732/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation](https://mlsanity.com/paper/1805.04574/) (2018)
- [Leveraging Uncertainty Estimates for Predicting Segmentation Quality](https://mlsanity.com/paper/1807.00502/) (2018)
- [3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes](https://mlsanity.com/paper/1607.00582/) (2016)
- [Weakly- and Semi-Supervised Panoptic Segmentation](https://mlsanity.com/paper/1808.03575/) (2018)
- [Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/1506.04924/) (2015)
- [Mutual Exclusivity Loss for Semi-Supervised Deep Learning](https://mlsanity.com/paper/1606.03141/) (2016)
- [Liver lesion segmentation informed by joint liver segmentation](https://mlsanity.com/paper/1707.07734/) (2017)
- [Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification](https://mlsanity.com/paper/2007.09337/) (2020)
- [Automatic Liver Lesion Detection using Cascaded Deep Residual Networks](https://mlsanity.com/paper/1704.02703/) (2017)

## Built on by (most cited first, 15 of 15)

- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval](https://mlsanity.com/paper/2403.15992/) (2024)
- [EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting](https://mlsanity.com/paper/2407.01029/) (2024)
- [DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.09918/) (2024)
- [GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting](https://mlsanity.com/paper/2407.01301/) (2024)
- [Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline](https://mlsanity.com/paper/2506.05175/) (2025)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels](https://mlsanity.com/paper/2409.01691/) (2024)
- [Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization](https://mlsanity.com/paper/2509.20785/) (2025)
- [Hint-dynamic Knowledge Distillation](https://mlsanity.com/paper/2211.17059/) (2022)
- [KM-UNet KAN Mamba UNet for medical image segmentation](https://mlsanity.com/paper/2501.02559/) (2025)
- [Uncertainty-Guided Conservative Propagation for Structured Inference in Vessel Segmentation](https://mlsanity.com/paper/2605.20543/) (2026)

## Abstract (arXiv)

The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel bring a lot of ambiguity in vessel segmentation and sub-type recognition, especially for the low-contrast capillary regions. Furthermore, recent two-staged approaches would accumulate and even amplify these inaccuracies from the first-stage whole vessel segmentation into the second-stage sub-type vessel pixel-wise classification. Moreover, the scarcity of manual annotation in organ vessels poses another challenge. In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels. In addition, we propose an uncertainty-aware semi-supervised training framework to alleviate the annotation-hungry limitation of deep models. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.