Motivation: Spatial transcriptomics enables gene expression profiling within its spatial context in intact tissue sections. Existing workflows for segmentation, spatial annotation, and morphological analysis are often code-heavy and poorly integrated. This limits the joint analysis of spatial gene expression at a single-nucleus resolution, and corresponding nuclear morphology. Results: We present CardioSeg, a Python-based graphical interface for nuclei segmentation, spatial annotation, and interactive analysis of myocardial histology. CardioSeg integrates multi-threshold Cellpose-based segmentation with nuclei-level transcriptomic mapping and interactive visualisation. CardioSeg achieved robust segmentation performance across heterogeneous imaging conditions, with union-based inference outperforming the individual parameter configurations. For cell-type annotation, CardioSeg achieved 0.88 in accuracy and 0.85 in balanced accuracy against reference labels, while also resolving spatial heterogeneity not captured by spot-based approaches. Application to pressure-overloaded cardiac tissue revealed uncharacterized intra-ventricular variations in nuclear morphology, indicating the potential of CardioSeg to couple disease-specific nuclear morphology with the associated transcriptomics. Availability and Implementation: Source code is available at GitHub under the CC BY 4.0 license (https://github.com/SrijanKancherla/CardioSeg). A versioned release was archived in Zenodo (DOI: 10.5281/zenodo.20177171). Keywords: Spatial transcriptomics, nuclei segmentation, cardiac histology, single-cell annotation, bioimage analysis, interactive visualization
Kancherla, S. K., Melleby, A. O., Aronsen, J. M.
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