HistokatFusion combines the information from multiple images in histopathology using image registration. The technology has been used in biomarker research, AI development and to combine and evaluate different stains.
Wouter Bulten et al., Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard. Nature Scientific Reports, 2019
Maschenka CA Balkenhol et al., Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics, The Breast, 2021
C. Mercan et al., Virtual Staining for Mitosis Detection in Breast Histopathology, IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
J. Lotz, N. Weiss, J. vd Laak, S. Heldmann, High-resolution Image Registration of Consecutive and Re-stained Sections in Histopathology. submitted, arxiv preprint, 2021
J. Borovec et al., ANHIR: Automatic Non-Rigid Histological Image Registration Challenge, IEEE Transactions on Medical Imaging, 2020
J. Lotz, Combined Local and Global Image Registration and its Application to Large-Scale Images in Digital Pathology, Dissertation, University of Lübeck, 2020
Integrate HistokatFusion from Python, C++ or use our serverless compute infrastructure on-demand.
HistokatFusion has been validated on thousands of differently stained image pairs.
Upload slides (or use some of ours) and see the automatic image registration of whole slide images of HistokatFusion in action.
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