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Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology

Lirui Qi, Hongliang He, Tong Wang, Siwei Feng, Guohong Fu

TL;DR

This paper addresses the data scarcity challenge in nuclear instance segmentation for digital pathology by introducing IM-Diffusion, a diffusion-based augmentation framework that increases diversity in nuclear layouts and internuclear spatial relationships. It combines a Nuclear Migration Module (NMM) to simulate size-dependent nucleus movement with an Internuclear-regions Inpainting Module (IIM) to create varied spatial arrangements, using a diffusion generator conditioned on processed labels. The approach yields state-of-the-art results on CoNSeP and GLySAC in both segmentation and classification metrics, with notable improvements for minority classes and limited gains from simply enlarging the dataset. By enriching training data with diverse microenvironments, IM-Diffusion enhances downstream model generalization for pathology Nuclear Instance Segmentation tasks, offering practical benefits for diagnosis and research.

Abstract

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.

Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology

TL;DR

This paper addresses the data scarcity challenge in nuclear instance segmentation for digital pathology by introducing IM-Diffusion, a diffusion-based augmentation framework that increases diversity in nuclear layouts and internuclear spatial relationships. It combines a Nuclear Migration Module (NMM) to simulate size-dependent nucleus movement with an Internuclear-regions Inpainting Module (IIM) to create varied spatial arrangements, using a diffusion generator conditioned on processed labels. The approach yields state-of-the-art results on CoNSeP and GLySAC in both segmentation and classification metrics, with notable improvements for minority classes and limited gains from simply enlarging the dataset. By enriching training data with diverse microenvironments, IM-Diffusion enhances downstream model generalization for pathology Nuclear Instance Segmentation tasks, offering practical benefits for diagnosis and research.

Abstract

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.

Paper Structure

This paper contains 12 sections, 8 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Preliminaries
  4. Nuclear Migration Module
  5. Internuclear-regions Inpainting Module
  6. Experiments
  7. Experiments Settings
  8. Datasets.
  9. Implementation Details.
  10. Evaluation Metrics.
  11. Results
  12. Conclusion

Figures (2)

  • Figure 1: The IM-Diffusion framework overview. The IM-Diffusion framework processes instance labels through NMM to generate novel layouts $\mathcal{G}{\text{new}}$. Per-instance $n$ migration distances $x$ exhibit a negative correlation with nuclear sizes $m$. Subsequently, the IIM module processes structural labels $I$ derived from instance labels to create diverse internuclear spatial relationships. This is exemplified by Case 1-2 (overlapping configurations) and Case 3-4 (adhesive nuclear formations). The refined layouts $\mathcal{G}_{\text{new}}$, processed through IIM, serve as conditioning inputs for image synthesis models, generating image-label pairs that enhance downstream model generalization.
  • Figure 2: Qualitative comparison of instance-level augmentation methods versus IM-Diffusion on histopathology benchmarks. Boxed regions exhibit enhanced classification consistency with Ground Truth (GT) in both CoNSeP and GLySAC datasets.