PhD Defense by Guolan Lu

Event Details
  • Date/Time:
    • Monday October 17, 2016
      3:00 pm - 5:00 pm
  • Location: : Room E160, Health Sciences Research Building (HSRB), Emory
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Shannon Sullivan

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Summary Sentence: Quantitative Detection and Delineation of Head and Neck Cancer Using Hyperspectral Imaging and Machine Learning

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Guolan Lu
BME Ph.D. Defense Presentation

 
Date: Monday, October 17
Time: 3:30 p.m.
Location: Room E160, Health Sciences Research Building (HSRB), Emory
 
Advisor:
Baowei Fei, Ph.D. (Emory University and Georgia Institute of Technology)
 
Committee members:
Georgia Zhuo Chen, Ph.D. (Emory University)
John N. Oshinski, Ph.D. (Emory University and Georgia Institute of Technology)
Brian W. Pogue, Ph.D. (Dartmouth College)
May Dongmei Wang, Ph.D. (Georgia Institute of Technology and Emory University)
 
Quantitative Detection and Delineation of Head and Neck Cancer Using Hyperspectral Imaging and Machine Learning
 
Abstract:
Over 500,000 patients are diagnosed with head and neck squamous cell carcinoma worldwide each year. Most people who develop head and neck cancer have advanced disease at the time of diagnosis. Early cancer detection and subsequent surgical resection of tumor remain one of the most promising approaches to improve the survival and quality of life of patients. Hyperspectral imaging (HSI) has emerged as a promising optical modality for early cancer detection and image-guided surgery. The major advantage of HSI is that it is a noninvasive technology that does not require any contrast agent, and it combines wide-field imaging and spectroscopy to simultaneously attain both spatial and spectral information from an object in a non-contact way. The biochemical and morphological properties of the tissue change during disease progression, therefore hyperspectral images, which contain high-dimensional spectral information (spectral fingerprint) at each image point, can be analyzed for visualization, characterization, and quantification of the disease state in biological tissue. Machine learning-based quantitative analysis is critical to fully exploit the rich spectral-spatial information provided by HSI for cancer detection. This dissertation aims to investigate the potential of label-free HSI technology combined machine learning methods as a noninvasive diagnostic tool for quantitative detection and delineation of head and neck cancer. More specifically, this dissertation can be divided into two major parts: The first part aims at evaluating the diagnostic performance of HSI and machine learning algorithms to differentiate malignant and nonmalignant tissue in two preclinical animal models. The second part aims at the detection and delineation of head and neck cancer in a surgical animal model and in ex vivo fresh surgical specimen of human patients. Overall, this work demonstrates that HSI combined with machine learning provides a noninvasive and quantitative diagnostic tool for the detection and delineation of head and neck cancers, which could be translated into the clinic for early cancer detection and image-guided surgery.

 

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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Sep 27, 2016 - 1:22pm
  • Last Updated: Oct 3, 2016 - 10:30am