Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy.

Lensfree in-line holographic microscopy offers sub-micron resolution over a large field-of-view (e.g., ~24 mm2) with a cost-effective and compact design suitable for field use. However, it is limited to relatively low-density samples. To mitigate this limitation, we demonstrate an on-chip imaging approach based on pixel super-resolution and phase recovery, which iterates among multiple lensfree intensity measurements, each having a slightly different sample-to-sensor distance. By digitally aligning and registering these lensfree intensity measurements, phase and amplitude images of dense and connected specimens can be iteratively reconstructed over a large field-of-view of ~24 mm2 without the use of any spatial masks. We demonstrate the success of this multi-height in-line holographic approach by imaging dense Papanicolaou smears (i.e., Pap smears) and blood samples

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Novel methods for subcellular in vivo imaging of the cornea with the Rostock Cornea Module 2.0

The Rostock Cornea Module transforms a confocal laser scanning ophthalmoscope into a corneal confocal laser scanning microscope. In this thesis, an improved version, the Rostock Cornea Module 2.0, and its achieved results were demonstrated. These include a concave contact cap design to attenuate eye movements to improve 3D volume reconstruction, an oscillating focal plane to improve mosaicking of the subbasal nerve plexus, the integration of simultaneous optical coherence tomography, multiwavelength corneal imaging, the clinical usage, and the automated morphological characterization

HYPERSPECTRAL LINE-SCANNING MICROSCOPY FOR HIGH-SPEED MULTICOLOR QUANTUM DOT TRACKING

One of the challenges in studying protein interactions in live cells lies in the capacity to obtain both spatial and temporal information that is sufficient to extend existing knowledge of the dynamics and interactions, especially when tracking proteins at high density. Here we introduce a high-speed laser line-scanning hyperspectral microscope that is designed to track quantum dot labeled proteins at 27 frames/sec over an area of 28 um2 using 128 spectral channels spanning the range from 500 to 750 nm. This instrument simultaneously excites 8 species of quantum dots and employs a custom prism spectrometer and high speed EMCCD to obtain spectral information that is then used to distinguish and track individual probes at high density. These emitters are localized to within 10s of nm in each frame and reconstructed trajectories yield information of the protein dynamics and interactions. This manuscript describes the design, implementation, characterization, and application of a high-speed laser line-scanning hyperspectral microscope (HSM). The intended primary application is that of investigating the dynamics of transmembrane antibody receptors using quantum dot labeled immunoglobulin E (QD-IgE). Several additional examples demonstrate other advantages and applications of this method, including 3D hyperspectral imaging of live cells and hyperspectral superresolution imaging

Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation

Ophthalmic engineering:the development of novel instrumentation to further research in the field

The principle theme of this thesis is the advancement and expansion of ophthalmic research via the collaboration between professional Engineers and professional Optometrists. The aim has been to develop new and novel approaches and solutions to contemporary problems in the field. The work is sub divided into three areas of investigation; 1) High technology systems, 2) Modification of current systems to increase functionality, and 3) Development of smaller more portable and cost effective systems. High Technology Systems: A novel high speed Optical Coherence Tomography (OCT) system with integrated simultaneous high speed photography was developed achieving better operational speed than is currently available commercially. The mechanical design of the system featured a novel 8 axis alignment system. A full set of capture, analysis, and post processing software was developed providing custom analysis systems for ophthalmic OCT imaging, expanding the current capabilities of the technology. A large clinical trial was undertaken to test the dynamics of contact lens edge interaction with the cornea in-vivo. The interaction between lens edge design, lens base curvature, post insertion times and edge positions was investigated. A novel method for correction of optical distortion when assessing lens indentation was also demonstrated. Modification of Current Systems: A commercial autorefractor, the WAM-5500, was modified with the addition of extra hardware and a custom software and firmware solution to produce a system that was capable of measuring dynamic accommodative response to various stimuli in real time. A novel software package to control the data capture process was developed allowing real time monitoring of data by the practitioner, adding considerable functionality of the instrument further to the standard system. The device was used to assess the accommodative response differences between subjects who had worn UV blocking contact lens for 5 years, verses a control group that had not worn UV blocking lenses. While the standard static measurement of accommodation showed no differences between the two groups, it was determined that the UV blocking group did show better accommodative rise and fall times (faster), thus demonstrating the benefits of the modification of this commercially available instrumentation. Portable and Cost effective Systems: A new instrument was developed to expand the capability of the now defunct Keeler Tearscope. A device was developed that provided a similar capability in allowing observation of the reflected mires from the tear film surface, but with the added advantage of being able to record the observations. The device was tested comparatively with the tearscope and other tear film break-up techniques, demonstrating its potential. In Conclusion: This work has successfully demonstrated the advantages of interdisciplinary research between engineering and ophthalmic research has provided new and novel instrumented solutions as well as having added to the sum of scientific understanding in the ophthalmic field

Optical coherence tomography methods using 2-D detector arrays

Optical coherence tomography (OCT) is a non-invasive, non-contact optical technique that allows cross-section imaging of biological tissues with high spatial resolution, high sensitivity and high dynamic range. Standard OCT uses a focused beam to illuminate a point on the target and detects the signal using a single photodetector. To acquire transverse information, transversal scanning of the illumination point is required. Alternatively, multiple OCT channels can be operated in parallel simultaneously; parallel OCT signals are recorded by a two-dimensional (2D) detector array. This approach is known as Parallel-detection OCT. In this thesis, methods, experiments and results using three parallel OCT techniques, including full -field (time-domain) OCT (FF-OCT), full-field swept-source OCT (FF-SS-OCT) and line-field Fourier-domain OCT (LF-FD-OCT), are presented. Several 2D digital cameras of different formats have been used and evaluated in the experiments of different methods. With the LF-FD-OCT method, photography equipment, such as flashtubes and commercial DSLR cameras have been equipped and tested for OCT imaging. The techniques used in FF-OCT and FF-SS-OCT are employed in a novel wavefront sensing technique, which combines OCT methods with a Shack-Hartmann wavefront sensor (SH-WFS). This combination technique is demonstrated capable of measuring depth-resolved wavefront aberrations, which has the potential to extend the applications of SH-WFS in wavefront-guided biomedical imaging techniques

Automated Raman cytology system for cancer diagnostics

Raman spectroscopy is a promising optical diagnostic tool that can be applied to un-stained cells in order to detect changes in molecular composition. The data collected can be described as a chemical fingerprint of the sample under investigation. Thus it is very popular in the recent times to use Raman spectroscopy in cytology to increase diagnostic sensitivity and specificity for early stage cancer. In this thesis, I introduce an automated Raman cytology system for cancer diagnostics which integrates all the hardware and software in Micro-manager and operates them in a specific order. An autofocus algorithm for unstained cells and a three-dimensional morphology recovery algorithm are also investigated and contributed to the final automated system.With increasing usage of Raman cytology systems, automation is a solution to limit data variabilities which is a major problem at the moment. In addition, a higher throughput of cellular analysis and a reduction in manpower could be expected from the proposed automation system