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Proceedings Paper

Image registration with deep probabilistic classifiers: application in radiation therapy
Author(s): Alireza Sedghi; Gregory Salomons; Jean-David Jutras; Jim Gooding; John Schreiner; William M. Wells III; Parvin Mousavi
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131509 (16 March 2020); doi: 10.1117/12.2549775
Show Author Affiliations
Alireza Sedghi, Queen's Univ. (Canada)
Gregory Salomons, Cancer Ctr. of Southeastern Ontario (Canada)
Jean-David Jutras, Cancer Ctr. of Southeastern Ontario (Canada)
Jim Gooding, Cancer Ctr. of Southeastern Ontario (Canada)
John Schreiner, Cancer Ctr. of Southeastern Ontario (Canada)
William M. Wells III, Harvard Medical School (United States)
Parvin Mousavi, Queen's Univ. (Canada)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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