Deep-learning-enabled computational microscopy for rapid cancer detection

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dc.contributor.advisorVeeraraghavan, Ashoken_US
dc.creatorJin, Lingboen_US
dc.date.accessioned2024-01-22T22:25:57Zen_US
dc.date.available2024-01-22T22:25:57Zen_US
dc.date.created2024-05en_US
dc.date.issued2024-01-16en_US
dc.date.submittedMay 2024en_US
dc.date.updated2024-01-22T22:25:57Zen_US
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-05-01en_US
dc.description.abstractThe gold standard for cancer detection in diagnosis and treatment guidance is based on histopathology, the examination of cells under a microscope. However, the tissue in question will need to be removed from the patient, sectioned into very thin slices, and stained before it can be examined by a pathologist. This preparation process is time-consuming and labor-intensive. The first part of this thesis focuses on an ex vivo ultraviolet extended depth-of-field microscope that can rapidly image large areas of freshly resected tissue, providing histologic quality images without physical sectioning. To overcome challenges in directly imaging thick intact tissue, such as subsurface scattering, high tissue surface irregularities, and difficulties in histology interpretation, the proposed microscopy platform unifies UV surface excitation, end-to-end extended depth-of-field, and GAN-based virtual staining into a single, coherent pipeline. This microscope provides an inexpensive and easy-to-use alternative to standard histopathology. The second part of this thesis extends the capabilities of an in vivo high-resolution micro-endoscopy (HRME) by enabling 3D volume imaging. The HRME is a minimally invasive microscope with an imaging fiber that can be used alongside an endoscope and provides cellular-resolution histological images in real-time. However, the micro-endoscopy can only image the superficial layer of the tissue in 2D. By incorporating a custom-designed phase mask with the fiber, the new Mask-HRME system can perform volumetric imaging with variable focusing ability. This micro-endoscopy will enable physicians to look deeper into the tissue without performing more invasive procedures. In both of the projects, computational imaging methods that jointly design the optics and image processing algorithm enables these new microscopes to break the limit of conventional microscopy and capture more diagnostic information than was previously possible.en_US
dc.embargo.lift2025-05-01en_US
dc.embargo.terms2025-05-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationJin, Lingbo. "Deep-learning-enabled computational microscopy for rapid cancer detection." (2024) PhD diss., Rice University. https://hdl.handle.net/1911/115364en_US
dc.identifier.urihttps://hdl.handle.net/1911/115364en_US
dc.language.isoengen_US
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.en_US
dc.subjectComputational microscopyen_US
dc.subjectmicroendoscopyen_US
dc.subjectcancer imagingen_US
dc.titleDeep-learning-enabled computational microscopy for rapid cancer detectionen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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