Learning Canonical Coordinate Systems for Deformable Organs: A Common Coordinate Framework for the Mouse Intestine

Whole-organ tissue clearing and volumetric confocal microscopy have enabled cellular-resolution imaging of intact biological organs, generating multi-terabyte datasets that require new computational methods for standardized spatial analysis. Unlike the brain, the intestine exhibits substantial nonlinear deformation caused by tissue stretching, folding, and specimen preparation, preventing reliable anatomical correspondence across animals and limiting quantitative analysis of the enteric nervous system (ENS). Existing image registration approaches rely primarily on intensity matching or handcrafted landmarks and fail to preserve the hierarchical organization of intestinal tissue layers. Here, we present GutCCF, a learning- based Common Coordinate Framework (CCF) for the mouse intestine, formulating canonical coordinate learning as multi-task representation learning for deformable organs. We define the canonical space of the intestine as an intrinsic cylindrical parameterization C = U × V × W , where U , V , and W denote normalized longitudinal arc length, normalized circumferential position, and transmural depth (ordered tissue segment hierarchy), respectively. The CCF is a mapping Φ : Ω → C from a specimen-dependent image domain to a shared coordinate identity. Our multi-head 2.5D U-Net jointly predicts semantic tissue identity s(x) via cross-entropy and a continuous depth field d(x) via an RMSE objective. This depth-aware monotonic layer localization imposes an ordinal structure on the shared latent representation. Encoding each voxel’s position requires confidence within the transmural hierarchy, yielding features that remain anatomically ordered under arbitrary tissue deformation and enabling segmentation and coordinate assignments to reinforce one another. The model is further refined through an iterative human-in-the-loop protocol that incorporates expert corrections into training to improve boundary localization and structural consistency. GutCCF is trained and evaluated using more than 150 high-resolution confocal image volumes of optically cleared intestines, including jejunum, ileum, and colon, from 71 transgenic mice containing seven genetically defined enteric neuron subtypes. Image datasets are at 0.863 μm isotropic resolution, spanning specimen lengths of 0.5–30 mm and between 200 and 700 optical sections. The training dataset contains over 2487 expertly annotated image-mask pairs with hierarchical tissue annotations including longitudinal muscle, circular muscle, submucosa, glands, villi, and out-of-tissue regions. The resulting coordinate system assigns every voxel to a continuous canonical anatomical space, enabling standardized localization of neuronal morphology, tissue structures, and multimodal biological measurements. Experimental results demonstrate that jointly learning anatomical depth and semantic segmentation significantly improves tissue boundary localization, spatial consistency, and cross-sample correspondence compared with segmentation-only baselines, achieving 82.48% mean Dice, 70.74% mIoU, 81.85% recall, and 83.28% overall precision. The proposed framework produces a high-resolution three-dimensional reference representation of the mouse intestine and enteric nervous system and provides a scalable foundation for multimodal spatial atlases, computational anatomy, and organ-scale digital twins.