Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis

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dc.contributor.advisorKelly, Kevin
dc.contributor.committeeMemberWong, Stephen T.C.
dc.creatorWeng, Sheng
dc.date.accessioned2019-05-16T20:25:46Z
dc.date.available2019-05-16T20:25:46Z
dc.date.created2017-08
dc.date.issued2017-08-11
dc.date.submittedAugust 2017
dc.date.updated2019-05-16T20:25:47Z
dc.description.abstractCoherent anti-Stokes Raman scattering (CARS) imaging technique has demonstrated great potential in clinical diagnosis by providing cellular-level resolution images without using exogenous contrast agents. This thesis contributes to the formation of an optical fiber based signal collection scheme and an automated image analytics platform to translate CARS microscopy for clinical uses. First, I introduce the concept of CARS by showing original images acquired from thyroid and parathyroid tissues. Second, I describe the use of a customized optical fiber bundle to collect and differentiate forward and backward generated CARS signals that contain different structural information. Third, I demonstrate the feasibility of using deep learning algorithms to characterize and classify CARS images automatically. In particular, I apply transfer learning on the CARS images and achieve 89.2% prediction accuracy in differentiating normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma human lung images. The combination of an optical fiber based microendoscopy and deep learning image classification algorithm will facilitate CARS imaging for on-the-spot cancer diagnosis, allowing medical practitioners to obtain essential information in real time and accelerate clinical decision-making. Meanwhile, the thesis also shows the generality of the deep learning algorithm developed by classifying screening images generated in drug discovery. As an example, for automated classification of large volumes of high-content screening images for Alzheimer’s disease drug discovery, by applying similar transfer learning method on hyperphosphorylated tau images, I categorize drug hits into ineffective, partially-effective, and significantly-effective groups with high speed and accuracy.
dc.format.mimetypeapplication/pdf
dc.identifier.citationWeng, Sheng. "Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis." (2017) Diss., Rice University. <a href="https://hdl.handle.net/1911/105513">https://hdl.handle.net/1911/105513</a>.
dc.identifier.urihttps://hdl.handle.net/1911/105513
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectNonlinear imaging
dc.subjectBiophotonics
dc.subjectMicroscopy
dc.subjectOptical fibers
dc.subjectImage analysis
dc.subjectDeep learning
dc.subjectCancer diagnosis
dc.subjectDrug screening
dc.subject
dc.titleIntegrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis
dc.typeThesis
dc.type.materialText
thesis.degree.departmentApplied Physics
thesis.degree.disciplineNatural Sciences
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.majorApplied Physics/Electrical Eng
thesis.degree.nameDoctor of Philosophy
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