Imaging and Computational Methods for Exploring Sub-cellular Anatomy

The ability to create large-scale high-resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function. This is particularly important for brain tissue, where the majority of function occurs at the cellular and sub-cellular level. However, reconstructing tissue at sub-cellular resolution is a complex problem that requires new methods for imaging and data analysis. In this dissertation, I describe a prototype microscopy technique that can image large volumes of tissue at sub-cellular resolution. This method, known as Knife-Edge Scanning Microscopy (KESM), has an extremely high data rate and can capture large tissue samples in a reasonable time frame. We can therefore image complete systems of cells, such as whole small animal organs, in a matter of days. I then describe algorithms that I have developed to cope with large and complex data sets. These include methods for improving image quality, tracing filament networks, and constructing high-resolution anatomical models. These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high-throughput microscopy data. The resulting models of large-scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing

Our ability to produce human-scale bio-manufactured organs is critically limited by the need for vascularization and perfusion. For tissues of variable size and shape, including arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion has posed a major hurdle. Here, we introduce a model-driven design pipeline combining accelerated optimization methods for fast synthetic vascular tree generation and computational hemodynamics models. We demonstrate rapid generation, simulation, and 3D printing of synthetic vasculature in complex geometries, from small tissue constructs to organ scale networks. We introduce key algorithmic advances that all together accelerate synthetic vascular generation by more than 230-fold compared to standard methods and enable their use in arbitrarily complex shapes through localized implicit functions. Furthermore, we provide techniques for joining vascular trees into watertight networks suitable for hemodynamic CFD and 3D fabrication. We demonstrate that organ-scale vascular network models can be generated in silico within minutes and can be used to perfuse engineered and anatomic models including a bioreactor, annulus, bi-ventricular heart, and gyrus. We further show that this flexible pipeline can be applied to two common modes of bioprinting with free-form reversible embedding of suspended hydrogels and writing into soft matter. Our synthetic vascular tree generation pipeline enables rapid, scalable vascular model generation and fluid analysis for bio-manufactured tissues necessary for future scale up and production.Comment: 58 pages (19 main and 39 supplement pages), 4 main figures, 9 supplement figure

3D Architectural Analysis of Neurons, Astrocytes, Vasculature & Nuclei in the Motor and Somatosensory Murine Cortical Columns

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, the sheer density of filamentous processes in the neuropil significantly complicates analysis due to the difficulty of separating individual cells in a highly interconnected network of tightly woven cellular arbors. In order to solve these problems, a variety of methodologies were developed and validated for improved analysis of cortical anatomy. An enhanced immunofluorescent staining and imaging protocol was utilized to precisely locate specific functional regions within brain slices at high magnification and collect four-channel, complete cortical columns. A powerful deconvolution routine was established which collected depth variant PSFs using an optical phantom for image restoration. Fractional volume analysis (FVA) was used to provide preliminary data of the proportions of each stained component in order to statistically characterize the variability within and between the functional regions in a depth-dependent and depth-independent manner. Finally, using machine learning techniques, a supervised learning model was developed that could automatically classify neuronal and astrocytic nuclei within the large cortical column datasets based on perinuclear fluorescence. These annotated nuclei were then used as seed points within their corresponding fluorescent channel for cell individualization in a highly interconnected network. For astrocytes, this technique provides the first method for characterization of complex morphology in an automated fashion over large areas without laborious dye filling or manual tracing

Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT

Die Entwicklung von Synchrotronlichtquellen der dritten Generation hat die Grundlage für die Untersuchung der 3D-Struktur opaker Proben mit einer Auflösung im Mikrometerbereich und höher geschaffen. Dies führte zur Entwicklung der Röntgen-Synchrotron-Mikro-Computertomographie, welche die Schaffung von Bildgebungseinrichtungen zur Untersuchung von Proben verschiedenster Art förderte, z.B. von Modellorganismen, um die Physiologie komplexer lebender Systeme besser zu verstehen. Die Entwicklung moderner Steuerungssysteme und Robotik ermöglichte die vollständige Automatisierung der Röntgenbildgebungsexperimente und die Kalibrierung der Parameter des Versuchsaufbaus während des Betriebs. Die Weiterentwicklung der digitalen Detektorsysteme führte zu Verbesserungen der Auflösung, des Dynamikbereichs, der Empfindlichkeit und anderer wesentlicher Eigenschaften. Diese Verbesserungen führten zu einer beträchtlichen Steigerung des Durchsatzes des Bildgebungsprozesses, aber auf der anderen Seite begannen die Experimente eine wesentlich größere Datenmenge von bis zu Dutzenden von Terabyte zu generieren, welche anschließend manuell verarbeitet wurden. Somit ebneten diese technischen Fortschritte den Weg für die Durchführung effizienterer Hochdurchsatzexperimente zur Untersuchung einer großen Anzahl von Proben, welche Datensätze von besserer Qualität produzierten. In der wissenschaftlichen Gemeinschaft besteht daher ein hoher Bedarf an einem effizienten, automatisierten Workflow für die Röntgendatenanalyse, welcher eine solche Datenlast bewältigen und wertvolle Erkenntnisse für die Fachexperten liefern kann. Die bestehenden Lösungen für einen solchen Workflow sind nicht direkt auf Hochdurchsatzexperimente anwendbar, da sie für Ad-hoc-Szenarien im Bereich der medizinischen Bildgebung entwickelt wurden. Daher sind sie nicht für Hochdurchsatzdatenströme optimiert und auch nicht in der Lage, die hierarchische Beschaffenheit von Proben zu nutzen. Die wichtigsten Beiträge der vorliegenden Arbeit sind ein neuer automatisierter Analyse-Workflow, der für die effiziente Verarbeitung heterogener Röntgendatensätze hierarchischer Natur geeignet ist. Der entwickelte Workflow basiert auf verbesserten Methoden zur Datenvorverarbeitung, Registrierung, Lokalisierung und Segmentierung. Jede Phase eines Arbeitsablaufs, die eine Trainingsphase beinhaltet, kann automatisch feinabgestimmt werden, um die besten Hyperparameter für den spezifischen Datensatz zu finden. Für die Analyse von Faserstrukturen in Proben wurde eine neue, hochgradig parallelisierbare 3D-Orientierungsanalysemethode entwickelt, die auf einem neuartigen Konzept der emittierenden Strahlen basiert und eine präzisere morphologische Analyse ermöglicht. Alle entwickelten Methoden wurden gründlich an synthetischen Datensätzen validiert, um ihre Anwendbarkeit unter verschiedenen Abbildungsbedingungen quantitativ zu bewerten. Es wurde gezeigt, dass der Workflow in der Lage ist, eine Reihe von Datensätzen ähnlicher Art zu verarbeiten. Darüber hinaus werden die effizienten CPU/GPU-Implementierungen des entwickelten Workflows und der Methoden vorgestellt und der Gemeinschaft als Module für die Sprache Python zur Verfügung gestellt. Der entwickelte automatisierte Analyse-Workflow wurde erfolgreich für Mikro-CT-Datensätze angewandt, die in Hochdurchsatzröntgenexperimenten im Bereich der Entwicklungsbiologie und Materialwissenschaft gewonnen wurden. Insbesondere wurde dieser Arbeitsablauf für die Analyse der Medaka-Fisch-Datensätze angewandt, was eine automatisierte Segmentierung und anschließende morphologische Analyse von Gehirn, Leber, Kopfnephronen und Herz ermöglichte. Darüber hinaus wurde die entwickelte Methode der 3D-Orientierungsanalyse bei der morphologischen Analyse von Polymergerüst-Datensätzen eingesetzt, um einen Herstellungsprozess in Richtung wünschenswerter Eigenschaften zu lenken

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study

CAD-CAM workflow for the fabrication of bioscaffolds and porous auricular constructs with polycaprolactone using Ultimaker 2+

In recent years, the application of three-dimensional fabrication to fabricate customized porous scaffolds for cell culture has received much attention from the field of tissue engineering and plastic surgery. In this study, we applied a more publicly accessible 3D printer, Ultimaker 2+ with biodegradable polymer-polycaprolactone (PCL) to fabricate both three-dimensional bioscaffolds and auricular constructs (both solid and porous) prepared to fill the gap as potential solutions for both cartilage defects and microtia respectively by managing of the CAD-CAM workflow. As an overview, the modified CAD-CAM workflow was regarded as the uniform preparation fabricating types of scaffolds to identify the general printability of PCL with Ultimaker 2+. For bioscaffolds, limit test was performed on original scaffold, the resolution for printing scaffolds by PCL was identified as 600 microns by applying method of uniform scaling and limit approaching. For customized auricular constructs, we extract the model from MRI/CT scan and use its mirror image for the general shape of model building in a relatively customized way to fabricate solid auricular constructs. Boolean operation was then applied for fabricating the inner porous microstructure to fabricate porous auricular constructs. As there were no significant differences among three groups of filaments regarding the respective dimensions for both bioscaffolds (n=9 for each group: PCL, PLA and ABS) and customized auricular constructs(n=5 for both solid auricular constructs and porous auricular constructs) indicated by the P value(P>0.05) from ANOVA, The printing compatibility of PCL regarding each specific domain of scaffolds were identified. In Conclusion, our study had indicated a consistent CAD-CAM workflow for Ultimaker 2+ with PCL to fabricate three-dimensional bioscaffolds, solid auricular constructs and porous auricular constructs which could be potentially applied to fill the gap of cartilage engineering and microtia reconstruction through in-vitro cell culture, surgical simulation and in-situ cell culture respectively