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Using Machine Learning to Distinguish Brain Tumor Progression From Pseudoprogression on Routine MRI

#artificialintelligence

Cleveland Clinic is a non-profit academic medical center. Advertising on our site helps support our mission. For over a century, malignant brain tumors such as glioblastoma (GBM) have carried a dismal prognosis. The most recent substantial advance has been provided by surgical resection and chemoradiation followed by adjuvant temozolomide therapy. Yet a problem during the requisite post-treatment surveillance imaging is that the brain's reaction to heavy doses of radiation can mimic the appearance of true tumor progression on MRI (Figure 1).


Interpreting Line Drawings as Three-Dimensional Surfaces

Classics

We propose a computational model for interpreting line drawings as three dimensional surfaces, based on constraints on local surface orientation along extremal and discontinuity boundaries. Specific techniques are described for two key processes: recovering the three-dimensional conformation of a space curve (e.g., a surface boundary) from its two-dimensional projection in an image, and interpolating smooth surfaces from orientation constraints along extremal boundaries.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.