Ms. Ayushi Sinha Profile

Ayushi Sinha

Graduate Research Assistant

SPIE Involvement:

Author

Area of Expertise:

medical image analysis , segmentation , registration , deformable registration , CT , statistical modeling

Websites:

Personal Website

Currently working on developing deformable registration algorithms to improve endoscopic navigation in the nasal airway and sinuses, and also improved segmentation and modeling algorithms for shapes or structures extracted from images (e.g., CT images). In the past, I have worked on fast estimation of geodesic distances on shapes, detection of neurons in electron microscopy data.

I also have some industry experience from an internship at Intel Corporation in the summer of 2013. For more details on my experiences, go to cs.jhu.edu/~ayushis.

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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