elenacasiraghi/miaquant_distribution

MATLAB code for segmenting and analyzing markers

MIAQuant_distribution

MATLAB code for segmenting, quantifying, and analyzing stained microscopy images

We are now working on TMA (for european grant) and we are using TMA_MIAQuant_Learn

In the repo you find also the code for nuclei segmentation (codiceMatlab20190216_BestPerf). This code should be re-implemented by using Python for reducing the STRESS computational load

[0] E. Casiraghi, J. Gliozzo, B. R. B. Barricelli, A. Rizzi, B. E. Leone, B. Vergani (2019). ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling. BMC BioInformatics, 20 (733). DOI:10.1186/s12859-019-3285-4.

MIAQuant_Learn has the extended feature of color learning

[1] V. Huber, V. Vallacchi, V. Fleming, X. Hu, A. Cova, M. Dugo, E. Shahaj, R. Sulsenti, E. Vergani, P. Filipazzi, A. De Laurentiis, L. Lalli, L. Di Guardo, R. Patuzzo, B. Vergani, E. Casiraghi, M. Cossa, A. Gualeni, V. Bollati, F. Arienti, F. De Braud, L. Mariani, A. Villa, P. Altevogt, V. Umansky, M. Rodolfo, L. Rivoltini (August 2018). Tumor-derived microRNAs induce myeloid suppressor cells and predict immunotherapy resistance in melanoma. Journal of Clinical Investigation, 128(12):5505-5516. doi: 10.1172/JCI98060.

[2] E. Casiraghi, V. Huber, M. Frasca, M. Cossa, M. Tozzi, L. Rivoltini, B.E. Leone, A. Villa, B. Vergani (2018). A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC BioInformatics, 19 (Suppl 10):357. doi: https://doi.org/10.1186/s12859-018-2302-3.

Original method: MIAQuant

[3] E. Casiraghi, M. Cossa, V. Huber, M. Tozzi, L. Rivoltini, A. Villa, B. Vergani (Nov. 2017). MIAQuant, a novel system for automatic segmentation, measurement, and localization comparison of different biomarkers from serialized histological slices. European Journal of Histochemistry, vol. 61 (4):2838. doi: https://doi.org/10.4081/ejh.2017.2838.