# Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet

arXiv: https://arxiv.org/abs/2006.14702 · HTML: https://mlsanity.com/paper/2006.14702/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.10989/) (2021)
- [RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01737/) (2022)
- [Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound](https://mlsanity.com/paper/2503.20685/) (2025)
- [Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning](https://mlsanity.com/paper/2107.14476/) (2021)
- [On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation](https://mlsanity.com/paper/2203.03587/) (2022)
- [Expectation Maximization Pseudo Labels](https://mlsanity.com/paper/2305.01747/) (2023)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [Interactive Whole Slide Images for RL-based Tumour Segmentation](https://mlsanity.com/paper/2608.16607/) (2026)

## Abstract (arXiv)

Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep convolutional neural network (CNN) to segment the catheter, which is expensive and time-consuming. In this paper, we propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method, but nevertheless achieves better performance. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation and which can efficiently localize the target catheter. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data. To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions. Experiments show the proposed scheme achieves a higher performance than state-of-the-art semi-supervised methods, while it demonstrates that our method is able to learn from large-scale unlabeled images.