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Graduate Student at Rice

Mary (Lingbo) Jin

Hi, my name is Mary and I am a 2nd-year graduate student at Rice Computational Imaging Lab advised by Professor Ashok Veeraraghavan. My research interest is in leveraging computational imaging for seeing below the skin applications. Specifically, I’m interested in jointly designing the optical system and post-processing algorithm to improve the resolution and contrast of clinical microscopy. My projects are done in collaboration with Dr.Yubo Tang from Rice Optical Spectroscopy & Imaging Lab.

Education & Experience

Rice University

Graduate Student

Department of ECE

Present

Genentech

Intern

Summer 2018

Carnegie Mellon University

Bachelor of Science

Department of ECE

Department of Biomedical Engineering

2014-2018

Recent Projects

2018-2019

Structured Illumination for Low Scattering Imaging in Low-cost Microscopy

Micro-endoscopy is a GI cancer screening tool that can provide cellular resolution images in real-time. It works in conjunction with endoscopy and can reduce the need for biopsy. However, subsurface tissue scattering creates undesirable background on the image, reducing the contrast. While there are many systems such as point- scanning confocal and 2-photon microscopy that can reject scattering background, these system are typically very slow and expensive due to the optomechanical parts. We are proposing a structured illumination microscopy that is able to reject scattering while acquiring images in real time. We test background rejection ability of our system both theoretically and experimentally and show that it provides low scattering images while costing a fraction of the price of a traditional confocal system.

2017-2018

Vessel Tracking in Ultra-high Frequency Ultrasound Imaging

Ultra High-Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand. Intricate vessel-based measurements, such as intimal wall thickness and vessel wall compliance, require sub-millimeter vessel tracking between B-scans. Our fast GPU-based approach combines the advantages of local phase analysis, a distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly segment and track the deforming vessel contour. We validated on 35 UHFUS sequences of vessels in the hand, and we show the transferability of the approach to 5 more diverse datasets acquired by a traditional High-Frequency Ultrasound (HFUS) machine. To the best of our knowledge, this is the first algorithm capable of rapidly segmenting and tracking deformable vessel contours in 2D UHFUS images. It is also the fastest and most accurate system for 2D HFUS images.

 

Tejas Sudharshan Mathai, Lingbo Jin, Vijay Gorantla, John M. Galeotti:
Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images. MICCAI (4) 2018: 746-754

Awards

Rice University

President’s Prize

Fall 2018

Rice University

John Clark, Jr. Fellowship Award

Fall 2018

Carnegie Mellon University

Summer Undergraduate Research Fellowship

Summer 2017

American Heart Association

Carnegie Heart Fellowship

Summer 2016

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