Spatial interaction analysis with graph based mathematical morphology for histopathology

Bassem Ben Cheikh, Nicolas Elie, Benoit Plancoulaine, Catherine Bor-Angelier, Daniel Racoceanu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)


Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.
Idioma originalEspañol
Título de la publicación alojadaProceedings - International Symposium on Biomedical Imaging
Número de páginas5
EstadoPublicada - 15 jun. 2017
Publicado de forma externa

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