# Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology

arXiv: https://arxiv.org/abs/2504.01577 · HTML: https://mlsanity.com/paper/2504.01577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [Diffusion-based Data Augmentation for Nuclei Image Segmentation](https://mlsanity.com/paper/2310.14197/) (2023)
- [DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets](https://mlsanity.com/paper/2306.14132/) (2023)
- [Unleashing the Power of Prompt-driven Nucleus Instance Segmentation](https://mlsanity.com/paper/2311.15939/) (2023)
- [NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models](https://mlsanity.com/paper/2303.11477/) (2023)
- [Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework](https://mlsanity.com/paper/2212.10305/) (2022)
- [Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models](https://mlsanity.com/paper/2407.14426/) (2024)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts](https://mlsanity.com/paper/2212.13780/) (2022)
- [Reflection-mode virtual histology using photoacoustic remote sensing microscopy](https://mlsanity.com/paper/2009.10010/) (2020)

## Abstract (arXiv)

Nuclear instance segmentation plays a vital role in disease diagnosis within digital pathology. However, limited labeled data in pathological images restricts the overall performance of nuclear instance segmentation. To tackle this challenge, we propose a novel data augmentation framework Instance Migration Diffusion Model (IM-Diffusion), IM-Diffusion designed to generate more varied pathological images by constructing diverse nuclear layouts and internuclear spatial relationships. In detail, we introduce a Nuclear Migration Module (NMM) which constructs diverse nuclear layouts by simulating the process of nuclear migration. Building on this, we further present an Internuclear-regions Inpainting Module (IIM) to generate diverse internuclear spatial relationships by structure-aware inpainting. On the basis of the above, IM-Diffusion generates more diverse pathological images with different layouts and internuclear spatial relationships, thereby facilitating downstream tasks. Evaluation on the CoNSeP and GLySAC datasets demonstrate that the images generated by IM-Diffusion effectively enhance overall instance segmentation performance. Code will be made public later.