Texture-­Based Computational Models of Biomedical Tissue in Radiological Images: Digital Tissue Atlases and Correlation with Genomics
Adrien Depeursinge, EPFL STI LIB

Seminar • 29 September 2014 • BM 4 233

Abstract
Modern multi–dimensional imaging in radiology yields much more information than the naked eye can appreciate. Computerized quantitative image analysis can make better use of the image content by yielding exhaustive, comprehensive and reproducible analysis of imaging features, which spawned the new field of “radiomics”. It has the potential to surrogate and surpass invasive biopsy–based molecular assays with the ability to capture intralesional heterogeneity in a non–invasive way. Radiomics is not a mature field of study, though. First, current computational models of biomedical tissue in multi–dimensional radiological protocols lack the appropriate framework for leveraging local image directions, which showed to be most relevant to characterize the geometry of 3–D biomedical texture. Second, most approaches proposed in the literature are assuming that the tissue properties are homogeneous over the tumors or organs, which is inadequate in most cases. We developed computational models of multi-­‐dimensional morphological properties of biomedical tissue. The Riesz transform and support vector machines are used to learn the organization of image scales and directions that is specific to a given biomedical tissue type. The models obtained can be “steered” analytically to enable rotation-­‐covariant image analysis. While most rotation-­‐invariant approaches discard precious information about image directions, rotation-­‐covariant analysis enables modeling the local organization of image directions independently from their local orientation. Experimental evaluation revealed high classification accuracies for even orders of the Riesz transform, and suggested high robustness to changes in global image orientation and illumination. The proposed computational models were able to fit a wide range of textures and tissue structures. Future work includes the optimization of the steerable texture models to enable more flexible template designs with both continuous scale characterization and compact support. The models will be located in organ anatomy to create personalized phenotyping of diseases and estimate underlying genomic signatures. These digital organ models can be used to diagnose, assess treatment response, and predict prognosis with higher precision.