Browsing by Author "Weng, Sheng"

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    Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis
    (2017-08-11) Weng, Sheng; Kelly, Kevin; Wong, Stephen T.C.
    Coherent 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.
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    Label-free Imaging of Thyroid and Parathyroid Glands Using Coherent Anti-Stokes Raman Scattering (CARS) Microscopy
    (2015-04-28) Weng, Sheng; Kelly, Kevin; Wong, Stephen; Thomann, Isabell; Kono, Junichiro
    Thyroid and parathyroid glands play a vital role in regulating the body's metabolism and calcium levels. Surgical removal of the glands is the main treatment for both thyroid cancer and parathyroid adenoma. In thyroidectomy and parathyroidectomy, it's very important to differentiate thyroid, parathyroid, and the other tissues around the neck. Traditionally, physicians use ultrasound guided fine needle aspiration (FNA) to evaluate thyroid nodules, but up to 30% of FNA results are “inconclusive”. The sestamibi scan can localize parathyroid adenoma, but currently it only has 50% accuracy. Here we applied the emerging CARS technique to image both thyroid and parathyroid tissues, which has potential to be used in real-time in vivo examination of different structures. We also developed algorithms to differentiate different cellular structures based on CARS images. When incorporated with a fiber optic endoscope in the future, CARS imaging technique can help surgeons identify cancerous thyroid tissue intraoperatively, preserve good parathyroid glands during thyroidectomy and find parathyroid adenoma during parathyroidectomy.