Abstract
Spatial transcriptomics (ST) platforms simultaneously measure gene expression and capture
cell nucleus images at single-cell resolution.
A fundamental property of tissue preparation introduces a confounding relationship between
the two measurement modalities: sectioning a three-dimensional tissue into a thin slice
produces a two-dimensional cross-section whose area depends on the cutting angle.
Larger cross-sections expose more cytoplasm, yielding higher mRNA counts, while
pathomics features such as nuclear area are measured directly from the same 2D boundary.
This sectioning geometry creates an apparent correlation between gene expression and
pathomics that reflects the measurement process rather than independent biology.
Existing gene-to-image methods generate cell images from gene features but do not support
condition-driven editing of existing images across both omics layers.
We apply the Manifold-Aware Diffusion (MAD) framework to this dual-omics
editing setting, enabling targeted modification of one feature group while preserving the
other.
Evaluated on podocyte cells from CosMx SMI kidney tissue, MAD achieves
MAE=0.6636 and FID=25.19 for pathomics editing, outperforming all compared baselines.
A controlled simulation study further confirms that the VAE component in MAD better
preserves cross-group independence than CCDM (MAD without VAE).
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MISO_002.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=xcYDYJ7Cj2
BibTex
@InProceedings{YanYue_From_MICCAISAT2026,
author = { Yang, Yuechen AND Zhu, Junchao AND Guo, Junlin AND Zhu, Yanfan AND Deng, Ruining AND Lu, Zhengyi AND Yao, Tianyuan AND Qu, Chongyu AND Lionts, Marilyn AND Guo, Xingyi AND Wang, Yaohong AND Wang, Yu AND Zhao, Shilin AND Yang, Haichun AND Huo, Yuankai},
title = { { From Cross-Section to Cross-Omics: Conditional Cell Nucleus Image Editing in Spatial Transcriptomics } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17252},
month = {pending},
page = {pending}
}