# Transforming the Interactive Segmentation for Medical Imaging

arXiv: https://arxiv.org/abs/2208.09592 · HTML: https://mlsanity.com/paper/2208.09592/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning](https://mlsanity.com/paper/2104.12166/) (2021)
- [Iterative Interaction Training for Segmentation Editing Networks](https://mlsanity.com/paper/1807.08555/) (2018)

## Built on by (most cited first, 9 of 9)

- [AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation](https://mlsanity.com/paper/2309.00096/) (2023)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation](https://mlsanity.com/paper/2510.10160/) (2025)
- [GenMask: Adapting DiT for Segmentation via Direct Mask Generation](https://mlsanity.com/paper/2603.23906/) (2026)
- [PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation](https://mlsanity.com/paper/2308.13746/) (2023)
- [DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation](https://mlsanity.com/paper/2604.12411/) (2026)
- [Interactive Instance Annotation with Siamese Networks](https://mlsanity.com/paper/2505.03184/) (2025)
- [Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation](https://mlsanity.com/paper/2510.20596/) (2025)

## Abstract (arXiv)

The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.