Published code for immunohistochemical image segmentation and co-occurrence analysis
MIAQuant, MIAQuant_learn, and TMA_MIAQuant_Learn are fully described in:
"A novel computational method for automatic segmentation, quantification and comparative analysis of histochemical and immunohistochemical serial slices." Elena Casiraghi, Mara Cossa, Matteo Tozzi, Licia Rivoltini, Antonello Villa and Barbara Vergani. BMC BioInformatics, 2018, 19 (10): 75-91
"MIAQuant, a novel system for automatic segmentation, measurement, and localization comparison of different biomarkers from serialized histological slices" E Casiraghi, M Cossa, V Huber, M Tozzi, L Rivoltini, A Villa, B Vergani European Journal of Histochemistry; 2017: 61 (4)
The usage of MIAQuant/ MIAQuant_Learn for automated system for ki67 segmentation and counting (code in codiceMatlabBestPerf) is described in: "ki67 Nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling" BR Barricelli, E Casiraghi, J Gliozzo,et al. BMC bioinformatics, 2019 20 (1), 733
MIAQuant and all its versions have been used to quantify all the histochemical images for the experiments, whose results are reported in "Tumor-derived microRNAs induce myeloid suppressor cells and predict immunotherapy resistance in melanoma" V Huber, V Vallacchi, V Fleming, et al. The Journal of clinical investigation, 2018, 128 (12): 5505-5516
MIAQuant* code is freely available for adaptation to other clinical studies, pathological research, and diagnosis. For any improvement, suggestion and help please write to: [email protected]
If any of MIAQuant versions are helpful for your studies/research, please cite the above mentioned article.