Image registration with deep probabilistic classifiers: application in radiation therapy

Alireza Sedghi; Gregory Salomons; Jean-David Jutras; Jim Gooding; John Schreiner; William M. Wells III; Parvin Mousavi

doi:10.1117/12.2549775

16 March 2020 Image registration with deep probabilistic classifiers: application in radiation therapy

Alireza Sedghi, Gregory Salomons, Jean-David Jutras, Jim Gooding, John Schreiner, William M. Wells III, Parvin Mousavi

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131509 (2020) https://doi.org/10.1117/12.2549775
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We present the application of deep multi-class classifiers for registration of the pre-radiation image (CBCT) to the treatment planning image (planCT) in Radiation Therapy (RT). We train a multi-class classifier on different classes of displacement between 3D patches of images and use it for registration. As the initial displacement between images might be large, we train multiple classifiers for different resolutions of the data to capture larger displacements in coarser resolutions. We show that having only a few patients, the deep multi-class classifiers enable an accurate and fast rigid registration for CBCT to planCT even with significantly different fields of view. Our work lays the foundation for deformable image registration and prediction of registration uncertainty which can be utilized for adaptive RT.

Conference Presentation

Citation Download Citation

Alireza Sedghi, Gregory Salomons, Jean-David Jutras, Jim Gooding, John Schreiner, William M. Wells III, and Parvin Mousavi "Image registration with deep probabilistic classifiers: application in radiation therapy", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131509 (16 March 2020); https://doi.org/10.1117/12.2549775

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