Title

Deep Learning Real-Time Adaptive Physics-based Non-Rigid Registration for Accurate Geometry Representation of Brain in Modeling Deformation During Glioma Resection

Presenting Author Name/s

Angelos Angelopoulos

Faculty Advisor

Dr. Nikos Chrisochoides

Presentation Type

Oral Presentation

Disciplines

Computer Sciences | Numerical Analysis and Scientific Computing | Theory and Algorithms

Description/Abstract

The Physics-based Non-Rigid Registration (PBNRR) framework allows for accurate real-time medical image registration and geometry representation of the brain in modeling deformation during glioma resection. Existing adaptive PBNRR (APBNRR) shows promise in being able to be utilized in time-constrained image-guided neurosurgery operations, but the issue of determining patient-specific input parameters to allow for optimal registration remains an open problem. We present a deep feedforward neural network that can predict sets of possible optimal or suboptimal input parameters that lead to a low Hausdorff distance of the registered image from the preoperative image. The neural network is trained on output produced by over 2.6 million retrospective APBNRR executions consisting of an almost exhaustive parameter study using cloud computing on 13 patient cases spanning from partial to excessive tumor resection. By utilizing the neural network, we can greatly reduce the parameter space that needs to be evaluated with APBNRR in order to achieve optimal results, and initial experiments have been very promising.

Session Title

Chemistry

Location

Learning Commons @ Perry Library Conference Room 1310

Start Date

2-2-2019 11:30 AM

End Date

2-2-2019 12:30 PM

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Feb 2nd, 11:30 AM Feb 2nd, 12:30 PM

Deep Learning Real-Time Adaptive Physics-based Non-Rigid Registration for Accurate Geometry Representation of Brain in Modeling Deformation During Glioma Resection

Learning Commons @ Perry Library Conference Room 1310

The Physics-based Non-Rigid Registration (PBNRR) framework allows for accurate real-time medical image registration and geometry representation of the brain in modeling deformation during glioma resection. Existing adaptive PBNRR (APBNRR) shows promise in being able to be utilized in time-constrained image-guided neurosurgery operations, but the issue of determining patient-specific input parameters to allow for optimal registration remains an open problem. We present a deep feedforward neural network that can predict sets of possible optimal or suboptimal input parameters that lead to a low Hausdorff distance of the registered image from the preoperative image. The neural network is trained on output produced by over 2.6 million retrospective APBNRR executions consisting of an almost exhaustive parameter study using cloud computing on 13 patient cases spanning from partial to excessive tumor resection. By utilizing the neural network, we can greatly reduce the parameter space that needs to be evaluated with APBNRR in order to achieve optimal results, and initial experiments have been very promising.