Histopathological image analysis with connections to genomics

The fields of imaging and genomics in cancer research have been mostly studied independently, but recently available datasets have made investigation into the synergy of these two fields possible. This work demonstrates the efficacy of computational histopathological image analysis to extract meaningful quantitative nuclear and cellular features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide images, significantly more data representing the variation in nuclear characteristics and tumor heterogeneity is available, which can aid in developing new analytical tools, such as the proposed convolutional neural network for nuclear segmentation, which produces state-of-the-art segmentation results on challenging cases seen in normal pathology. This robust segmentation tool is essential for capturing reliable features for computational pathology. Additionally, whole slide images capture rich spatial information about tumors, which presents a challenge, but also an opportunity for the development of new image processing tools to capture this spatial information, which could be considered for future work. Other histopathological image modalities and relevant machine learning tools are also considered for elucidating cellular processes of cancer

Evaluation of stereo matching for mobile platforms with applications for assisting the visually impaired

The rising interest in immersive entertainment and enhanced image and video content, along with the development of stereo cameras for mobile platforms, motivates the presented evaluation of stereo matching algorithms for mobile devices. This work investigates this potential for stereo matching on a mobile device for real-time applications, in terms of computation time and quality of depth inference. Several algorithms are tested on an Android tablet housing a Tegra 3 processor using images captured from the on-board consumer-grade cameras. Despite distortions incurred by the lower quality cameras and the computational constraints of a tablet, results show that a simple block matching approach can perform reasonable inference at a rate of 10 frames-per-second. Other methods are shown to be too computationally demanding for real-time applications, as even the fastest alternative local method, using adaptive support weights, requires up to 20 seconds per frame on a 320x360 image. Results also show the impact of lower quality ``real-world'' images on inference performance on algorithms. Additionally, real-time stereo matching on a mobile device is applied to a novel application of assisting the visually impaired with navigation. A system is proposed using a simple block matching algorithm that infers the depth of the scene and communicates the presence of obstacles via sound to the user. The system is housed entirely within the mobile device, overcoming a primary hindrance of many users of assistive technology. The use of a mobile device also allows for an intuitive, interactive experience for the user with the depth information directly via the touch screen of the device. This system demonstrates the added functionality of real-time depth estimation on a mobile device and the potential for aiding the visually impaired with navigation

Ultrasensitive Three-Dimensional Orientation Imaging of Single Molecules on Plasmonic Nanohole Arrays Using Second Harmonic Generation

Recently, fluorescence-based super-resolution techniques such as stimulated emission depletion (STED) and stochastic optical reconstruction microscopy (STORM) have been developed to achieve near molecular-scale resolution. However, such a super-resolution technique for nonlinear label-free microscopy based on second harmonic generation (SHG) is lacking. Since SHG is label-free and does not involve real-energy level transitions, fluorescence-based super-resolution techniques such as STED cannot be applied to improve the resolution. In addition, due to the coherent and non-isotropic emission nature of SHG, single-molecule localization techniques based on isotropic emission of fluorescent molecule such as STORM will not be appropriate. Single molecule SHG microscopy is largely hindered due to the very weak nonlinear optical scattering cross sections of SHG scattering processes. Thus, enhancing SHG using plasmonic nanostructures and nanoantennas has recently gained much attention owing to the potential of various nanoscale geometries to tightly confine electromagnetic fields into small volumes. This confinement provides substantial enhancement of electromagnetic field in nanoscale regions of interest, which can significantly boost the nonlinear signal produced by molecules located in the plasmonic hotspots. However, to date, plasmon-enhanced SHG has been primarily applied for the measurement of bulk properties of the materials/molecules, and single molecule SHG imaging along with its orientation information has not been realized yet. Herein, we achieved simultaneous visualization and three-dimensional (3D) orientation imaging of individual rhodamine 6G (R6G) molecules in the presence of plasmonic silver nanohole arrays. SHG and two-photon fluorescence microscopy experiments together with finite-difference time-domain (FDTD) simulations revealed a ∼10-fold nonlinear enhancement factor at the hot spots on the plasmonic silver nanohole substrate, enabling detection of single molecules using SHG. The position and 3D orientation of R6G molecules were determined using the template matching algorithm by comparing the experimental data with the calculated dipole emission images. These findings could enable SHG-based single molecule detection and orientation imaging of molecules which could lead to a wide range of applications from nanophotonics to super-resolution SHG imaging of biological cells and tissues