Microscopy with ultraviolet surface excitation for rapid slide-free histology.

Histologic examination of tissues is central to the diagnosis and management of neoplasms and many other diseases, and is a foundational technique for preclinical and basic research. However, commonly used bright-field microscopy requires prior preparation of micrometre-thick tissue sections mounted on glass slides, a process that can require hours or days, that contributes to cost, and that delays access to critical information. Here, we introduce a simple, non-destructive slide-free technique that within minutes provides high-resolution diagnostic histological images resembling those obtained from conventional haematoxylin-and-eosin-histology. The approach, which we named microscopy with ultraviolet surface excitation (MUSE), can also generate shape and colour-contrast information. MUSE relies on ~280-nm ultraviolet light to restrict the excitation of conventional fluorescent stains to tissue surfaces, and it has no significant effects on downstream molecular assays (including fluorescence in situ hybridization and RNA-seq). MUSE promises to improve the speed and efficiency of patient care in both state-of-the-art and low-resource settings, and to provide opportunities for rapid histology in research

Quantitative analysis with machine learning models for multi-parametric brain imaging data

Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping

Doctor of Philosophy

dissertationHuman retinitis pigmentosa (RP) typically involves decades of progressive vision loss before some patients become blind, and prospective therapies target patients who have been blind for substantial time, even decades. Evaluations of molecular and cellular therapies have primarily employed short-lived mouse models lacking the scope of remodeling common in human RP. The Rho Tg P347L transgenic rabbit offers a unique opportunity to evaluate the primary degeneration event and subsequent progressive remodeling that ensues over a timespan that recapitulates the human disease phenotype. Retinas from a TgP347L rabbit model of human dominant RP and wild-type litter mates were harvested over an 8-year span and processed for transmission electron microscope connectomics, immunocytochemistry for a range of macromolecules, and computational molecular phenotyping for small molecules, including transport tracing with D-Asp. Early time points in the TgP347L rabbit recapitulate the established sequence of photoreceptor loss, retinal remodeling, and reprogramming, and also reveal progressive disruptions in Müller cell metabolism, where rather than observing a homogeneous glial population, chaotic metabolic signatures emerge. By 4 years, virtually all remnants of photoreceptors are gone and the neural retina manifests severe cell loss and near complete loss of glutamine synthetase, though glial glutamate transport persists. By 6 years, there is a global >90% neuronal loss. In some regions the retina is devoid of identifiable cells and replaced by unknown debris-like assemblies. Though the 6-year retina does have locations with recognizable neurons, all cell types are drastically reduced in number and some have altered metabolic phenotypes. These results are never seen in wt littermates, including rabbits which are 8 years old. Electron microscopic analysis using wide-field connectomics imaging of the 6-year TgP347L sample demonstrates some structurally normal synapses, indicating that survivor neurons in these regions are not quiescent despite the lack of sensory input for a substantial period of time. These results indicate that, although photoreceptor degeneration is the trigger, retinal remodeling ultimately gives way to neurodegeneration, which is a separate unrelenting disease process independent of the initial insult, closely resembling slow progressive CNS neurodegenerations. Indeed, both metabolic disruption and debris-related degeneration predicts the existence of a persistent neuropathy, and increases in ?-synuclein levels support a proteinopathy component. Remodeling and neurodegeneration progress until the retina is devoid of recognizable cells. There is no stable state into which the retina settles and no cell type is spared. This has profound implications for current therapeutics. There will likely be critical windows for implementation but, ultimately, suspension of neurodegenerative remodeling will be required for long-term success

Studying biological tissue with fluorescence lifetime imaging: microscopy, endoscopy, and complex decay profiles

We have applied fluorescence lifetime imaging (FLIM) to the autofluorescence of different kinds of biological tissue in vitro, including animal tissue sections and knee joints as well as human teeth, obtaining two-dimensional maps with functional contrast. We find that fluorescence decay profiles of biological tissue are well described by the stretched exponential function (StrEF), which can represent the complex nature of tissue. The StrEF yields a continuous distribution of fluorescence lifetimes, which can be extracted with an inverse Laplace transformation, and additional information is provided by the width of the distribution. Our experimental results from FLIM microscopy in combination with the StrEF analysis indicate that this technique is ready for clinical deployment, including portability that is through the use of a compact picosecond diode laser as the excitation source. The results obtained with our FLIM endoscope successfully demonstrated the viability of this modality, though they need further optimization. We expect a custom-designed endoscope with optimized illumination and detection efficiencies to provide significantly improved performance. © 2003 Optical Society of America.Peer Reviewe

Fast widefield techniques for fluorescence and phase endomicroscopy

Thesis (Ph.D.)--Boston UniversityEndomicroscopy is a recent development in biomedical optics which gives researchers and physicians microscope-resolution views of intact tissue to complement macroscopic visualization during endoscopy screening. This thesis presents HiLo endomicroscopy and oblique back-illumination endomicroscopy, fast widefield imaging techniques with fluorescence and phase contrast, respectively. Fluorescence imaging in thick tissue is often hampered by strong out-of-focus background signal. Laser scanning confocal endomicroscopy has been developed for optically-sectioned imaging free from background, but reliance on mechanical scanning fundamentally limits the frame rate and represents significant complexity and expense. HiLo is a fast, simple, widefield fluorescence imaging technique which rejects out-of-focus background signal without the need for scanning. It works by acquiring two images of the sample under uniform and structured illumination and synthesizing an optically sectioned result with real-time image processing. Oblique back-illumination microscopy (OBM) is a label-free technique which allows, for the first time, phase gradient imaging of sub-surface morphology in thick scattering tissue with a reflection geometry. OBM works by back-illuminating the sample with the oblique diffuse reflectance from light delivered via off-axis optical fibers. The use of two diametrically opposed illumination fibers allows simultaneous and independent measurement of phase gradients and absorption contrast. Video-rate single-exposure operation using wavelength multiplexing is demonstrated

Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection And Plaque Characterization

The importance of atherosclerotic disease in coronary artery has been a subject of study for many researchers in the past decade. In brief, the aim is to understand progression of such a disease, detect plaques at risks (vulnerable plaques), and treat them selectively to prevent mortality and immobility. Consequently, several imaging modalities have been developed and among them intravascular ultrasound (IVUS) has been of particular interest since it provides useful information about tissues microstructures and images with sufficient penetration as well as resolution. In general, the ultimate goal is to provide interventional cardiologists with reliable clinical tools so they can identify vulnerable plaques, make decisions confidently, choose the most appropriate drugs or implant devices (i.e. stent), and stabilize them during catheterization procedures with minimal risk. In this work, we review existing atherosclerotic tissue characterization algorithms including the state-of-the-art virtual histology (VH) framework, which has been implemented in the Volcano (Rancho Cordova, CA) IVUS clinical scanners using 64-elements 20 MHz phased-array transducer. Initially, we intended to extend this technique for data acquired with 40 MHz single-element transducers. For this reason, we started acquiring in vitro IVUS data and studied involved challenges from specimen preparation toward classification. We observed inconsistency among extracted features along with transducer's spectral parameters (i.e. bandwidth, center frequency). This, in addition to infeasibility of construction of reliable training dataset due to heterogeneity of atherosclerotic tissues motivated us to develop an unsupervised texture-based atherosclerotic tissue characterization algorithm. We proposed a two-dimensional multiscale wavelet-based algorithm to expand IVUS backscattered signals and/or grayscale images onto orthogonal symmetric quadrature mirror filters (QMF) such as Lemarie-Battle. At the bottom of decomposition tree, we employed ISODATA to cluster enveloped detected features in an unsupervised fashion and classify atherosclerotic plaque constitutes into fibrotic, lipidic, calcified, and no tissues. For the first time, we studied numbers of factors that were necessary for extension of in vitro derived classifier for in vivo applications such as reliability of classified tissues behind arc of calcified plaques and effects of pressure changes as well as flowing blood on constructed tissue color maps, called prognosis histology (PH) images. The second half of this dissertation is devoted to automatic detection of lumen borders in IVUS grayscale images acquired with high frequency (40 MHz up) transducers where more scattering exhibited within lumen area that makes the problem of interest more challenging. We established our framework on three-dimensional expansion of IVUS sub-volumes onto orthonormal brushlet basis function. The rational behind our framework was presence of incoherent (i.e. blood) versus coherent (i.e. plaque, surrounding fat) textural patterns along pullback direction, which was motivated by what an interventional cardiologist does to locate the lumen border visually by going back and forth among IVUS frames. We studied the feasibility of brushlet analysis through filtering blood speckles and supervised classification of blood versus non-blood regions. Our preliminary study confirmed that the most informative features reside in the innermost cubes, representing low-frequency components in transformed domain. Finally, we explored that tissue responses to IVUS signals are proportionally preserved in brushlet coefficients and it enabled us to classify blood regions in complex brushlet space. Subsequently, we employed surface function actives (SFA) to estimate the lumen borders after regularization. In a comparison study, we quantified our results with two of existing algorithms, employing IVUS grayscale images acquired with 40 MHz and 45 MHz single-element transducers. Overall, our proposed algorithm outperformed and the resulting automated detected borders showed good correlation with manually traced borders by an expert

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Data driven approaches for investigating molecular heterogeneity of the brain

It has been proposed that one of the clearest organizing principles for most sensory systems is the existence of parallel subcircuits and processing streams that form orderly and systematic mappings from stimulus space to neurons. Although the spatial heterogeneity of the early olfactory circuitry has long been recognized, we know comparatively little about the circuits that propagate sensory signals downstream. Investigating the potential modularity of the bulb’s intrinsic circuits proves to be a difficult task as termination patterns of converging projections, as with the bulb’s inputs, are not feasibly realized. Thus, if such circuit motifs exist, their detection essentially relies on identifying differential gene expression, or “molecular signatures,” that may demarcate functional subregions. With the arrival of comprehensive (whole genome, cellular resolution) datasets in biology and neuroscience, it is now possible for us to carry out large-scale investigations and make particular use of the densely catalogued, whole genome expression maps of the Allen Brain Atlas to carry out systematic investigations of the molecular topography of the olfactory bulb’s intrinsic circuits. To address the challenges associated with high-throughput and high-dimensional datasets, a deep learning approach will form the backbone of our informatic pipeline. In the proposed work, we test the hypothesis that the bulb’s intrinsic circuits are parceled into distinct, parallel modules that can be defined by genome-wide patterns of expression. In pursuit of this aim, our deep learning framework will facilitate the group-registration of the mitral cell layers of ~ 50,000 in-situ olfactory bulb circuits to test this hypothesis

Depth-Resolved Assessment of Atherosclerosis by Intravascular Photoacoustic-Ultrasound Imaging

Coronary heart disease is the leading cause of death in the United States and the incidence is projected to increase by 18% by 2030. Yet, there remains a pressing clinical need for tools to detect vulnerable atherosclerotic plaques that can rupture and lead to major adverse cardiac events. Plaques that are considered most vulnerable for rupture are thin-capped fibroatheromas, which are grossly defined by hallmarks of a thin fibrous cap, a large lipid-rich necrotic core, inflammatory infiltrate, and positive remodeling. These plaques are often structurally non-obstructive to moderately obstructive, thus asymptomatic and clinically unidentifiable with routine angiography and stress testing. Rather, their vulnerability is a product of their chemical composition. We have developed a dual-mode intravascular catheter which is capable of producing co-registered cross-sectional images of arterial wall morphology and lipid content, via ultrasound and photoacoustic modes, respectively. Validation of this capability will rely on interrogation of atherosclerotic coronary arteries from humans and peripheral arteries from swine, with comparison to gold-standard histopathology and competing technologies. Here, we present ex vivo validation of a novel intravascular photoacoustic-ultrasound (IVPA-US) imaging catheter and the first systematic in vivo IVPA-US imaging study in a preclinical swine model with native disease, necessary benchmarks before proceeding with translation to clinic. We aim to ultimately demonstrate predictive utility to detect plaques that are vulnerable to rupture and trigger adverse cardiac events. In addition, this will be instrumental in elucidating the mechanism of plaque rupture, the development of preventive and therapeutic interventions, and reducing coronary heart disease-related mortality