A neural network approach for fast, automated quantification of DIR performance

Abstract

The formulation presented in this paper demonstrates the ability for fast, accurate quantification of registration performance. DNN provided the necessary level of abstraction to estimate a quantified TRE from the ISM expectations described above, when sufficiently trained on annotated data. In addition, biomechanical models facilitated the DNN with the required variations in the patient posture and physiological regression. With further development and validation on clinical patient data, such networks have potential impact in patient and site-specific optimization, and stream-lining clinical registration validation.