Dr. Austin Reiter Profile

Dr. Austin Reiter

at Johns Hopkins Univ

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Author

Publications (8)

Proceedings Article | 19 February 2018 Paper

Exploring the effects of transducer models when training convolutional neural networks to eliminate reflection artifacts in experimental photoacoustic images

Derek Allman, Austin Reiter, Muyinatu Bell

Proceedings Volume 10494, 104945H (2018) https://doi.org/10.1117/12.2291279

KEYWORDS: Receivers, Data modeling, Transducers, Photoacoustic spectroscopy, Acoustics, Interference (communication), Convolutional neural networks, Wavefronts, Ultrasonography, Photoacoustic imaging

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Proceedings Article | 3 March 2017 Paper

A machine learning approach to identifying point source locations in photoacoustic data

Austin Reiter, Muyinatu Lediju Bell

Proceedings Volume 10064, 100643J (2017) https://doi.org/10.1117/12.2255098

KEYWORDS: Deep learning, Convolutional neural networks, Image restoration, Neural networks, Photoacoustic imaging, Visualization, Acoustics, Machine learning, Wavefronts, Surgery, Biopsy, Ultrasonography, Signal detection, Imaging systems, Image resolution

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Proceedings Article | 24 February 2017 Presentation + Paper

Simultaneous segmentation and correspondence improvement using statistical modes

Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory Hager, Russell Taylor

Proceedings Volume 10133, 101331B (2017) https://doi.org/10.1117/12.2253533

KEYWORDS: Statistical modeling, Image registration, Image segmentation, Error analysis, Shape analysis, Principal component analysis, Image processing algorithms and systems, Surgery, Medical imaging, Diagnostics

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Proceedings Article | 21 March 2016 Paper

Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations

Ayushi Sinha, Simon Leonard, Austin Reiter, Masaru Ishii, Russell Taylor, Gregory Hager

Proceedings Volume 9784, 97840D (2016) https://doi.org/10.1117/12.2217337

KEYWORDS: Image segmentation, Statistical modeling, Tissues, Image registration, Computed tomography, Statistical analysis, Endoscopy, Skull, Surgery, Image processing, Video

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Proceedings Article | 21 March 2016 Paper

Image-based navigation for functional endoscopic sinus surgery using structure from motion

Simon Leonard, Austin Reiter, Ayushi Sinha, Masaru Ishii, Russell Taylor, Gregory Hager

Proceedings Volume 9784, 97840V (2016) https://doi.org/10.1117/12.2217279

KEYWORDS: Endoscopes, Endoscopy, Surgery, Video, Image registration, Augmented reality, Computed tomography, Navigation systems, Image segmentation, 3D image processing

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Functional Endoscopic Sinus Surgery (FESS) is a challenging procedure for otolaryngologists and is the main surgical approach for treating chronic sinusitis, to remove nasal polyps and open up passageways. To reach the source of the problem and to ultimately remove it, the surgeons must often remove several layers of cartilage and tissues. Often, the cartilage occludes or is within a few millimeters of critical anatomical structures such as nerves, arteries and ducts. To make FESS safer, surgeons use navigation systems that register a patient to his/her CT scan and track the position of the tools inside the patient. Current navigation systems, however, suffer from tracking errors greater than 1 mm, which is large when compared to the scale of the sinus cavities, and errors of this magnitude prevent from accurately overlaying virtual structures on the endoscope images. In this paper, we present a method to facilitate this task by 1) registering endoscopic images to CT data and 2) overlaying areas of interests on endoscope images to improve the safety of the procedure. First, our system uses structure from motion (SfM) to generate a small cloud of 3D points from a short video sequence. Then, it uses iterative closest point (ICP) algorithm to register the points to a 3D mesh that represents a section of a patients sinuses. The scale of the point cloud is approximated by measuring the magnitude of the endoscope's motion during the sequence. We have recorded several video sequences from five patients and, given a reasonable initial registration estimate, our results demonstrate an average registration error of 1.21 mm when the endoscope is viewing erectile tissues and an average registration error of 0.91 mm when the endoscope is viewing non-erectile tissues. Our implementation SfM + ICP can execute in less than 7 seconds and can use as few as 15 frames (0.5 second of video). Future work will involve clinical validation of our results and strengthening the robustness to initial guesses and erectile tissues.

Proceedings Article | 21 March 2016 Paper

Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery

A. Reiter, S. Leonard, A. Sinha, M. Ishii, R. Taylor, G. Hager

Proceedings Volume 9784, 978418 (2016) https://doi.org/10.1117/12.2216296

KEYWORDS: Endoscopy, 3D modeling, Surgery, Neural networks, Image processing, Reflectivity, Image analysis, 3D image processing, Bidirectional reflectance transmission function, Cameras, Atomic force microscopy

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Proceedings Article | 18 March 2015 Paper

Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery

Y. Otake, S. Leonard, A. Reiter, P. Rajan, J. Siewerdsen, G. Gallia, M. Ishii, R. Taylor, G. Hager

Proceedings Volume 9415, 94150A (2015) https://doi.org/10.1117/12.2081732

KEYWORDS: Endoscopes, Image registration, Computed tomography, Endoscopy, Surgery, Optimization (mathematics), Cameras, Image-guided intervention, Stochastic processes, 3D modeling

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Proceedings Article | 5 October 2007 Paper

Local track repair for video tracking on small UAVs

Stephen DelMarco, Matthew Antone, Austin Reiter, Todd Jenkins

Proceedings Volume 6736, 673610 (2007) https://doi.org/10.1117/12.739616

KEYWORDS: Video, Video surveillance, Unmanned aerial vehicles, Detection and tracking algorithms, Cameras, Signal to noise ratio, Data acquisition, Analytical research, Kinematics, Sensors

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Showing 5 of 8 publications

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