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Spatial Diffusion for Cell Layout Generation

Chen Li, Xiaoling Hu, Shahira Abousamra, Meilong Xu, Chao Chen

arXiv:2409.03106v1cs.CV

TL;DR

Cell detection depends on costly labeled data, and generative approaches have largely overlooked the spatial patterns needed for realistic cell layouts. The paper proposes diffusion-based layout generation guided by spatial density features, then uses generated layouts to condition pathology-image generation. The authors report that GMM performs best among the explored density models for BRCA-M2C, and that generated pathology images can improve cell-detection performance.

  • Problem

    Cell detection requires costly labeled data, while existing generative approaches largely overlook the spatial distributions needed to generate realistic layouts containing many cells.

    Source: §1 · p. 1

  • Method

    A diffusion model conditions on cell-count categories and jointly generates cell layouts and spatial density maps, with KDE, GMM, and GMCM explored as density models.

    Source: §1 · p. 2

  • Results

    For BRCA-M2C, GMM achieves the best layout-generation performance among the explored density models, and generated pathology images can improve cell-detection performance.

    Source: §4.1 · p. 8, §5 · p. 9

  • Takeaways & Limitations

    Generated layouts can condition pathology-image generation, and the resulting synthetic images can augment supervised cell-detection methods.

    Source: §1 · p. 2, §3.1 · p. 5

Abstract

from arXiv · show

Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell detection instead, i.e., locating and classifying cells in given pathology images. One important information that has been largely overlooked is the spatial patterns of the cells. In this paper, we propose a spatial-pattern-guided generative model for cell layout generation. Specifically, a novel diffusion model guided by spatial features and generates realistic cell layouts has been proposed. We explore different density models as spatial features for the diffusion model. In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images. Augmenting with these images can significantly boost the performance of SOTA cell detection methods. The code is available at https://github.com/superlc1995/Diffusion-cell.

1 Introduction

Cell detection needs costly labeled data, while generative models have largely overlooked the spatial distributions needed to represent realistic layouts of many cells. The paper proposes a counting-conditioned diffusion model that jointly generates layouts and density maps, explores density models, and uses generated layouts to guide pathology-image generation for cell-detection augmentation.

1 Introduction

  • Cell detection requires costly labeled data, and existing generative models largely overlook cell spatial patterns needed for realistic layouts with hundreds or thousands of cells.Existing methods either generate layouts randomly or reuse existing layouts.Source: §1 · p. 1
  • The model conditions diffusion on cell-count categories and jointly generates cell layouts and spatial density maps to learn heterogeneous spatial distributions.It represents each cell as a square marker in a binary mask.Source: §1 · p. 2
  • The paper compares KDE, GMM, and GMCM as density models and introduces spatial-FID, which compares generated and real layouts in spatial representation space.The authors characterize KDE as flexible, GMM as more constrained, and GMCM as a compromise.Source: §1 · p. 2
  • Generated layouts guide pathology-image generation, and the resulting synthetic images can augment supervised cell-detection methods.The authors report that this augmentation can boost cell-detection performance.Source: §1 · p. 2

2 Related work

Prior layout-generation work includes GAN-, VAE-, and diffusion-based approaches, while diffusion models have also been applied across several generation tasks. This paper focuses on cell layouts and pathology-image generation, where cell-detection layouts provide only center coordinates rather than full object geometry.

Diffusion Model

  • Diffusion models have been applied to segmentation, point-cloud generation, video generation, super-resolution, classifier-guided generation, and crowd-distribution alignment.The paper distinguishes its focus as cell-layout and pathology-image generation for cell detection.Source: §2 · pp. 2–3

Layout Generation

  • The proposed framework conditions layout generation on cell-count categories and incorporates spatial density maps during training and generation.Figure 1 depicts one cell type and labels the layout and density map at time step t as x^p_t and x^d_t.Source: Figure 1 · p. 3
  • Layout-generation methods include LayoutGAN, LayoutVAE, and diffusion-based LayoutDM, but these methods require object locations and sizes unavailable in cell-detection layouts.TMCCG attempts cell-layout generation with GANs; the paper instead constructs a diffusion-based framework.Source: §2 · p. 3

3 Method

The method generates cell layouts with a diffusion model guided by spatial density information, then uses those layouts to condition pathology-image generation. It models layout sparsity and cell-count variation while exploring three spatial-feature models.

3.1 Layout and Image Generation

  • Cells are represented by narrow 3 × 3 square markers centered on the cells, preventing dense layouts from collapsing into a single mass.Generated layouts follow the corresponding spatial-density generation pattern.Source: §3 · p. 4, §3.1 · p. 5
  • Spatial density maps are concatenated with sparse layout maps during training to teach the diffusion model the layouts’ underlying spatial distribution.Each cell type’s density maps are generated independently.Source: §3.1 · p. 4
  • Cell-count categories are formed by dividing the training-set count distribution into quantile intervals, then sampling equally from each category at inference.Directly conditioning on exact cell counts is avoided because few samples per count can yield poor-quality layouts.Source: §3.1 · p. 5
  • Generated pathology-image diffusion is conditioned on cell layout maps, which guide the cell distribution in the synthetic images.The resulting images and layouts can augment existing cell-detection methods.Source: §3.1 · p. 5

3.2 Spatial Feature Extractors

  • KDE, GMM, and GMCM are explored as spatial-feature extractors for cluster position, density, and area.KDE smooths point contributions with a bandwidth trade-off between detail and noise versus smoothness and oversmoothing; Scott’s Rule selects the bandwidth.Source: §3.2 · pp. 5–6
  • GMM models cluster patterns parametrically, while GMCM uses a Gaussian-mixture copula to capture dependencies between marginal densities.An EM algorithm optimizes both models, and BIC selects their component counts.Source: §3.2 · p. 6

4 Experiment

The experiments evaluate spatial-FID layout quality and cell-detection augmentation on BRCA-M2C, with GMM-based layouts performing best in the reported comparisons. Ablations examine counting categories, while the evaluation setup defines the datasets, models, and metrics.

4 Experiment

  • The evaluation uses BRCA-M2C, spatial-FID for generated layouts, and F-scores for cell detection.BRCA-M2C contains 80 training, 10 validation, and 30 test patches; the study generates 200 layouts and corresponding pathology images per counting category.Source: §4 · pp. 6–7, §4.1 · p. 7
  • Spatial-FID replaces natural-image visual features with spatial features from the middle of an autoencoder trained on layout maps.The metric is a modification of FID; lower values are better.Source: §4 · pp. 6–7

4.1 Results

  • GMM-generated density maps achieve the best spatial-FID results, and GMM-directed pathology images boost cell-detection performance best.The authors attribute this to BRCA-M2C layouts matching Gaussian mixtures; Table 2 reports detection F-scores for inflammatory, epithelial, stromal, and all cells.Source: Table 1 · p. 7, Table 2 · p. 7, §4.1 · p. 8

4.2 Ablation study

  • Counting categories are important for realistic layouts, and the method remains robust when the number of categories changes.The ablation compares omitting the condition with using 10 or 20 categories.Source: §4.2 · p. 8, Table 3 · p. 9

5 Conclusion

The conclusion presents spatial-distribution-guided diffusion for generating cell layouts and pathology images. It reports that GMM performs best among the explored density tools and that generated images can improve cell-detection performance.

5 Conclusion

  • The framework explores KDE, GMM, and GMCM for spatial features; GMM performs best because BRCA-M2C layouts conform most closely to Gaussian mixtures.Generated layouts condition pathology-image generation, and the resulting images improve SOTA cell-detection methods.Source: §5 · p. 9

6 Supplementary

The supplementary studies KDE bandwidth selection and GMM component count, then presents additional layouts, pathology images, and density maps. The authors report that Scott’s Rule is more valid for KDE and that BIC selection is essential for GMM.

Bandwidth.

  • Scott’s Rule is reported as more valid than constant bandwidth choices for extracting spatial information from cell layouts.The supplementary table studies KDE bandwidth; bandwidth controls the balance between detail and smoothing, with low values risking noise and high values risking oversmoothing.Source: Bandwidth · p. 9, Table 4 · p. 9

Number of components

6.2 Samples

  • The additional samples pair pathology images with cell layouts and generated density maps, which the authors say reflect cell spatial distributions well.The figure includes training-set examples and generations for counting categories 0 through 4.Source: §6.2 · p. 9, Figure 4 · p. 10

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