ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics
Journal:
arXiv
Published Date:
Dec 4, 2024
Abstract
Spatial transcriptomics (ST) is an emerging technology that enables medical
computer vision scientists to automatically interpret the molecular profiles
underlying morphological features. Currently, however, most deep learning-based
ST analyses are limited to two-dimensional (2D) sections, which can introduce
diagnostic errors due to the heterogeneity of pathological tissues across 3D
sections. Expanding ST to three-dimensional (3D) volumes is challenging due to
the prohibitive costs; a 2D ST acquisition already costs over 50 times more
than whole slide imaging (WSI), and a full 3D volume with 10 sections can be an
order of magnitude more expensive. To reduce costs, scientists have attempted
to predict ST data directly from WSI without performing actual ST acquisition.
However, these methods typically yield unsatisfying results. To address this,
we introduce a novel problem setting: 3D ST imputation using 3D WSI histology
sections combined with a single 2D ST slide. To do so, we present the
Anatomy-aware Spatial Imputation Graph Network (ASIGN) for more precise, yet
affordable, 3D ST modeling. The ASIGN architecture extends existing 2D spatial
relationships into 3D by leveraging cross-layer overlap and similarity-based
expansion. Moreover, a multi-level spatial attention graph network integrates
features comprehensively across different data sources. We evaluated ASIGN on
three public spatial transcriptomics datasets, with experimental results
demonstrating that ASIGN achieves state-of-the-art performance on both 2D and
3D scenarios. Code is available at https://github.com/hrlblab/ASIGN.
