Nicolás-Sáenz, LauraGuerrero Aspizua, SaraPascau González-Garzón, JavierMuñoz Barrutia, María Arrate2021-03-022021-03-022020-09Nicolás-Sáenz, L., Guerrero-Aspizua, S., Pascau, J., Muñoz-Barrutia, A. (2020). Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps. Entropy, 22(9), 946.1099-4300https://hdl.handle.net/10016/32066We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.19eng© 2020 by the authors.Atribución 3.0 EspañaComputational pathologyImage registrationAntigen segmentationCancerresearch articleBiología y Biomedicinahttps://doi.org/10.3390/e22090946open access1919Entropy22AR/0000026498