# Iterative Interaction Training for Segmentation Editing Networks

arXiv: https://arxiv.org/abs/1807.08555 · HTML: https://mlsanity.com/paper/1807.08555/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 14 of 14)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Interactive 3D Medical Image Segmentation with SAM 2](https://mlsanity.com/paper/2408.02635/) (2024)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2303.10692/) (2023)
- [Transforming the Interactive Segmentation for Medical Imaging](https://mlsanity.com/paper/2208.09592/) (2022)
- [UCP-Net: Unstructured Contour Points for Instance Segmentation](https://mlsanity.com/paper/2109.07592/) (2021)
- [Quality-Aware Memory Network for Interactive Volumetric Image Segmentation](https://mlsanity.com/paper/2106.10686/) (2021)
- [Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration](https://mlsanity.com/paper/2111.07716/) (2021)
- [From Sparse to Precise: A Practical Editing Approach for Intracardiac Echocardiography Segmentation](https://mlsanity.com/paper/2303.11041/) (2023)
- [PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation](https://mlsanity.com/paper/2308.13746/) (2023)
- [RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning](https://mlsanity.com/paper/2204.05951/) (2022)

## Abstract (arXiv)

Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency. Nevertheless often users want to edit the segmentation to their own needs and will need different tools for this. There has been methods developed to edit segmentations of automatic methods based on the user input, primarily for binary segmentations. Here however, we present an unique training strategy for convolutional neural networks (CNNs) trained on top of an automatic method to enable interactive segmentation editing that is not limited to binary segmentation. By utilizing a robot-user during training, we closely mimic realistic use cases to achieve optimal editing performance. In addition, we show that an increase of the iterative interactions during the training process up to ten improves the segmentation editing performance substantially. Furthermore, we compare our segmentation editing CNN (interCNN) to state-of-the-art interactive segmentation algorithms and show a superior or on par performance.