Thesis Proposal: Honghua Guan
Jul 6 @ 12:30 pm
Thesis Proposal: Honghua Guan

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: High-throughput Optical Explorer in Freely-behaving Rodents

Abstract: One critical goal for neuroscience is to explore the mechanisms underlying neuronal information processing. A suitable brain imaging tool is of great significance to be capable of recording clear neuronal signals over prolonged periods. Among different imaging modalities, multiphoton microscopy becomes the choice for in vivo brain applications owing to its subcellular resolution, optical sectioning and deep penetration. The current experimental routine, however, requires head-fixation of animals during data acquisition. This configuration will inevitably introduce unwanted stress and limit many behavior studies such as social interaction. The scanning two-photon fiberscope is a promising technical direction to bridge this gap. Benefiting from its ultra-compact design and light-weight, it is an ideal optical brain imaging modality to assess dynamic neuronal activities in freely-behaving rodents with subcellular resolution. One significant challenge with the compact scanning two-photon fiberscope is its suboptimal imaging throughput due to the limited choices of miniature optomechanical components.

In this project, we present a compact multicolor two-photon fiberscope platform. We achieve three-wavelength excitation by synchronizing the pulse trains from a femtosecond OPO and its pump. The imaging results demonstrate that we can excite several different fluorescent proteins simultaneously with an optimal excitation efficiency. In addition, we propose a deep neural network (DNN) based solution that significantly improves the imaging frame rate with minimal loss in image quality. This innovation enables 10-fold speed enhancement for the scanning two-photon fiberscope, making it feasible to perform video-rate (26 fps) two-photon imaging in freely-moving mice with excellent imaging resolution and SNR that were previously not possible.

Committee Members

  • Xingde Li, Department of Biomedical Engineering
  • Mark Foster, Department of Electrical and Computer Engineering
  • Jing U. Kang, Department of Electrical and Computer Engineering
  • Israel Gannot, Department of Electrical and Computer Engineering
  • Hui Lu, Department of Pharmacology and Physiology, George Washington University
Dissertation Defense: Debojyoti Biswas
Aug 9 @ 10:00 am
Dissertation Defense: Debojyoti Biswas

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Stochastic Models of Chemotaxing Signaling Processes

Abstract: Stochasticity is ubiquitous in all processes. Its contribution in shaping the output response is not only restricted to systems involving entities with low copy numbers. Intrinsic fluctuations can also affect systems in which the interacting species are present in abundance. Chemotaxis, the migration of cells towards chemical cues, is one such example. Chemotaxis is a fundamental process that is behind a wide range of biological events, ranging from the innate immune response of organisms to cancer metastasis. In this dissertation, we study the role that stochastic fluctuations play in the regulatory mechanism that regulates chemotaxis in the social amoeba Dictyostelium discoideum. It has been argued theoretically and shown experimentally that stochastically driven threshold crossings of an underlying excitable system, lead to the protrusions that enable amoeboid cells to move. To date, however, there has been no good computational model that accurately accounts for the effects of noise, as most models merely inject noise extraneously to deterministic models leading to stochastic differential equations. In contrast, in this study, we employ an entirely different paradigm to account for the noise effects, based on the reaction-diffusion master equation. Using a modular approach and a three-dimensional description of the cell model with specific subdomains attributed to the cell membrane and cortex, we develop a detailed model of the receptor-mediated regulation of the signal transduction excitable network (STEN), which has been shown to drive actin dynamics. Using this model, we recreate the patterns of wave propagation seen in both front- and back-side markers that are seen experimentally. Moreover, we recreate various perturbations. Our model provides further support for the biased excitable network hypothesis that posits that directed motion occurs from a spatially biased regulation of the threshold for activation of an excitable network.

Here we also consider another aspect of the chemotactic response. While front- and back-markers redistribute in response to chemoattractant gradients, over time, this spatial heterogeneity becomes established and can exist even when the external chemoattractant gradient is removed. We refer to this persistent segregation of the cell into back and front regions as polarity. In this dissertation, we study various methods by which polarity can be established. For example, we consider the role of vesicular trafficking as a means of bringing back-markers from the front to the rear of the cell. Then, we study how BAR-domain proteins that are sensitive to membrane curvature, can amplify small shape heterogeneities leading to cell polarization. Finally, we develop computational models that describe a novel framework by which polarity can be established and perturbed through the alteration of the charge distribution on the inner leaf of the cell membrane.

Committee Members

  • Pablo A. Iglesias, Department of Electrical and Computer Engineering
  • Noah J . Cowan, Department of Mechanical Engineering
  • Enrique Mallada, Department of Electrical and Computer Engineering
  • Peter N. Devreotes, Department of Cell Biology
Dissertation Defense: Yufan He
Aug 12 @ 1:00 pm
Dissertation Defense: Yufan He

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Retinal OCT Image Analysis Using Deep Learning

Abstract: Optical coherence tomography (OCT) is a noninvasive imaging modality which uses low coherence light waves to take cross-sectional images of optical scattering media. OCT has been widely used in diagnosing retinal and neural diseases by imaging the human retina. The thickness of retinal layers are important biomarkers for neurological diseases like multiple sclerosis (MS). The peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell plus inner plexiform layer (GCIP) thickness can be used to assess global disease progression of MS patients. Automated OCT image analysis tools are critical for quantitatively monitoring disease progression and explore biomarkers. With the development of more powerful computational resources, deep learning based methods have achieved much better performance in accuracy, speed, and algorithm flexibility for many image analysis tasks. However, without task-specific modifications, these emerging deep learning methods are not satisfactory if directly applied to tasks like retinal layer segmentation.

In this thesis, we present a set of novel deep learning based methods for OCT image analysis. Specifically, we focus on automated retinal layer segmentation from macular OCT images. A first problem we address is that existing deep learning methods do not incorporate explicit anatomical rules and cannot guarantee the layer segmentation hierarchy (pixels of the upper layers should have no overlap or gap with pixels of layers beneath it). To solve this, we developed an efficient fully convolutional network to generate structured layer surfaces with correct topology that is also able to perform retinal lesion (cysts or edema) segmentation. A second problem we addressed is that the segmentation uncertainty reduces the sensitivity of detecting mild retinal changes in MS patients overtime. To solve this, we developed a longitudinal deep learning pipeline that considers both inter-slice and longitudinal segmentation priors to achieve a more consistent segmentation for monitoring patient-specific retinal changes. A third problem we addressed is that the performance of the deep learning models will degrade when test data is generated from different scanners (domain shift). We address this problem by developing a novel test-time domain adaptation method. Different than existing solutions, our model can dynamically adapt to each test subject during inference without time-consuming retraining. Our deep networks achieved state-of-the-art segmentation accuracy, speed, and flexibility comparing to the existing methods.

Committee Members

  • Jerry Prince, Department of Electrical and Computer Engineering
  • Archana Venkataraman, Department of Electrical and Computer Engineering
  • Vishal Patel, Department of Electrical and Computer Engineering
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