TL;DR
A multimodal pseudo-ground truth training technique to enable supervised feature learning on H&E images for whole-cell segmentation and adapted into a ST pipeline to create an integrated end-to-end machine learning solution, without additional optimisation on ST data.
Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, where signals from multiple cells are aggregated and critical cellular heterogeneity is obscured. Extending this paradigm to single-cell reso...
Imaging-based spatial transcriptomics can resolve gene expression at subcellular level in intact tissue, but its value in human myocardium depends on accurate cell segmentation. Adult cardiomyocytes are large (100–150 µm), elongated, and often multinucleated; they bear no resemblance to the small round cells that...
It is argued that cell segmentation for spatial pathology should be evaluated not only by boundary and instance accuracy, but also by preservation of the biological relationships that downstream analyses are intended to measure.
Molecular assays such as spatial transcriptomics facilitate fine-grained identification of cell types for analyzing tumor microenvironments. However, they are expensive to acquire in routine clinical practice compared to hematoxylin and eosin (H&E) images. With molecular guidance, such images can distinguish cell linea...
Label-free transmitted-light (TL) imaging offers a non-invasive route to monitor live-cell responses in high-content screening, but robust segmentation and viability classification remain difficult because TL images have low intrinsic contrast, poorly defined cellular boundaries, and substantial morphological variabili...
Accurate nuclei segmentation in histopathology images is crucial for downstream biological analysis. While Transformer based models excel at capturing global contexts, they struggle with blurred boundaries and structural adhesion in dense cellular regions. To address this, we propose Swin-UNet-EARM, introducing an Edge...
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