A Deep Learning Approach for Motion Forecasting Using 4D OCT Data - PowerPoint PPT Presentation

Jun 21, 2023 •169 likes •230 views

Marcel Bengs, Nils Gessert, Alexander Schlaefer Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems Institute of Medical Technology and Intelligent Systems A Deep Learning Approach for Motion Forecasting

Marcel Bengs, Nils Gessert, Alexander Schlaefer Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems Institute of Medical Technology and Intelligent Systems A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
July 2020 Slide 2 Motivation: Motion Forecasting Radiotherapy Intraoperative Imaging Motion ROI Motion
July 2020 Slide 3 Deep Learning and Spatio-temporal OCT Optical Coherence Tomography (OCT) template 1 deep learning motion estimation (CNN) lag between the adjustment and the motion estimation moving state t motion estimation 4D deep learning motion forecasting OCT volumes over time We propose a deep learning approach for motion estimation and forecasting using sequences of OCT volumes 1 Gessert, Nils, et al. Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation. In: Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. International Society for Optics and Photonics, 2019. S. 1095108.
July 2020 Slide 4 Deep Learning Methods and Data Set 1 Data Set • Swept-source OCT device (OMES, OptoRes) • 40 different ROIs of a chicken breast sample • 100 different trajectories each 1 Gessert, Nils, et al. Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation. In: Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. International Society for Optics and Photonics, 2019. S. 1095108.
July 2020 Slide 5 Results and Discussion Using a stream of volumes improves estimation performance and allows for forecasting Motion Estimation t n Motion Forecasting t n+1 Motion Forecasting t n+2 2-Path-CNN3D (2-P-3D) n-Path-CNN3D (n-P-3D) CNN4D (CNN4D) n-Path-CNN4D (n-P-4D) GRU-CNN3D (GRUC3D)

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