Tumor angiogenesis assessment using multi-fluorescent scans on murine slices by Markov random field framework

Oumeima Laifa; Delphine Le Guillou-Buffello; Daniel Racoceanu

doi:10.1117/12.2285924

17 November 2017 Tumor angiogenesis assessment using multi-fluorescent scans on murine slices by Markov random field framework

Oumeima Laifa, Delphine Le Guillou-Buffello, Daniel Racoceanu

Author Affiliations +

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057208 (2017) https://doi.org/10.1117/12.2285924
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis crucial in assessing effect of anti-angiogenic therapies. Since many years, such therapies are designed to inhibit the vascular endothelial growth factor (VEGF). To contribute to the assessment of anti-angiogenic agent (Pazopanib) effect on vascular and cellular structures, we acquired data from tumors extracted from a murine tumor model using Multi- Fluorescence Scanning. In this paper, we implemented an unsupervised algorithm combining the Watershed segmentation and Markov Random Field model (MRF). This algorithm allowed us to quantify the proportion of apoptotic endothelial cells and to generate maps according to cell density. Stronger association between apoptosis and endothelial cells was revealed in the tumors receiving anti-angiogenic therapy (n = 4) as compared to those receiving placebo (n = 4). A high percentage of apoptotic cells in the tumor area are endothelial. Lower density cells were detected in tumor slices presenting higher apoptotic endothelial areas.

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Oumeima Laifa, Delphine Le Guillou-Buffello, and Daniel Racoceanu "Tumor angiogenesis assessment using multi-fluorescent scans on murine slices by Markov random field framework", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057208 (17 November 2017); https://doi.org/10.1117/12.2285924

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