# Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation

arXiv: https://arxiv.org/abs/2505.06210 · HTML: https://mlsanity.com/paper/2505.06210/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 9 May 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2311.17791/) (2023)
- [SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation](https://mlsanity.com/paper/2308.13759/) (2023)
- [Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks](https://mlsanity.com/paper/2101.06040/) (2021)
- [ConvFormer: Combining CNN and Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2211.08564/) (2022)
- [Boosting Medical Image Classification with Segmentation Foundation Model](https://mlsanity.com/paper/2406.11026/) (2024)
- [SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings](https://mlsanity.com/paper/2307.12429/) (2023)
- [Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8](https://mlsanity.com/paper/2502.00133/) (2025)

## Built on by (most cited first, 5 of 5)

- [TopoCL: Topological Contrastive Learning for Medical Imaging](https://mlsanity.com/paper/2603.14647/) (2026)
- [Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification](https://mlsanity.com/paper/2512.07190/) (2025)
- [Learning with Geometric Priors in U-Net Variants for Polyp Segmentation](https://mlsanity.com/paper/2601.17331/) (2026)
- [Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2605.17719/) (2026)
- [TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging](https://mlsanity.com/paper/2606.29763/) (2026)

## Abstract (arXiv)

Convolutional neural network (CNN) and Transformer-based architectures are two dominant deep learning models for polyp segmentation. However, CNNs have limited capability for modeling long-range dependencies, while Transformers incur quadratic computational complexity. Recently, State Space Models such as Mamba have been recognized as a promising approach for polyp segmentation because they not only model long-range interactions effectively but also maintain linear computational complexity. However, Mamba-based architectures still struggle to capture topological features (e.g., connected components, loops, voids), leading to inaccurate boundary delineation and polyp segmentation. To address these limitations, we propose a new approach called Topo-VM-UNetV2, which encodes topological features into the Mamba-based state-of-the-art polyp segmentation model, VM-UNetV2. Our method consists of two stages: Stage 1: VM-UNetV2 is used to generate probability maps (PMs) for the training and test images, which are then used to compute topology attention maps. Specifically, we first compute persistence diagrams of the PMs, then we generate persistence score maps by assigning persistence values (i.e., the difference between death and birth times) of each topological feature to its birth location, finally we transform persistence scores into attention weights using the sigmoid function. Stage 2: These topology attention maps are integrated into the semantics and detail infusion (SDI) module of VM-UNetV2 to form a topology-guided semantics and detail infusion (Topo-SDI) module for enhancing the segmentation results. Extensive experiments on five public polyp segmentation datasets demonstrate the effectiveness of our proposed method. The code will be made publicly available.