Visualization methods for analysis of 3D multi-scale medical data

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Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology. Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology. In this interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins. Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue

Design and development of optical reflectance spectroscopy and optical coherence tomography catheters for myocardial tissue characterization

Catheter ablation therapy attempts to restore sinus rhythm in arrhythmia patients by producing site-specific tissue modification along regions which cause abnormal electrical activity. This treatment, though widely used, often requires repeat procedures to observe long-term therapeutic benefits. This limitation is driven in part by challenges faced by conventional schemes in validating lesion adequacy at the time of the procedure. Optical techniques are well-suited for the interrogation and characterization of biological tissues. In particular, optical coherence tomography (OCT) relies on coherence gating of singly-scattered light to enable high-resolution structural imaging for tissue diagnostics and procedural guidance. Alternatively, optical reflectance spectroscopy (ORS) is a point measurement technique which makes use of incoherent, multiply-scattered light to probe tissue volumes and derive important data from its optical signature. ORS relies on the fact that light-tissue interactions are regulated by absorption and scattering, which directly relate to the intrinsic tissue biochemistry and cellular organization. In this thesis, we explore the integration of these modalities into ablation catheters for obtaining procedural metrics which could be utilized to guide catheter ablation therapy. We first present the development of an accelerated computational light transport model and its application for guiding ORS catheter design. A custom ORS-integrated ablation catheter is then implemented and tested within porcine specimens in vitro. A model is proposed for real-time estimation of lesion size based on changes in spectral morphology acquired during ablation. We then fabricated custom integrated OCT M-mode RF catheters and present a model for detecting contact status based on deep convolutional neural networks trained on endomyocardial images. Additionally, we demonstrate for the first time, tracking of RF-induced lesion formation employing OCT Doppler micro-velocimetry; this response is shown to be commensurate with the degree of treatment. We further demonstrate for the first time spectroscopic tracking of kinetics related to the heme oxidation cascade during thermal treatment, which are linked to tissue denaturation. The pairing of these modalities into a single RF catheter was also validated for guiding lesion delivery in vitro and within live pigs. Finally, we conclude with a proof-of-concept demonstration of ORS as a mapping tool to guide epicardial ablation in human donor hearts. These results showcase the vast potential of ORS and OCT empowered RF catheters for aiding intraprocedural guidance of catheter ablation procedures which could be utilized alongside current practices

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions

Development of a complete advanced computational workflow for high-resolution LDI-MS metabolomics imaging data processing and visualization

La imatge per espectrometria de masses (MSI) mapeja la distribució espacial de les molècules en una mostra. Això permet extreure informació Metabolòmica espacialment corralada d'una secció de teixit. MSI no s'usa àmpliament en la metabolòmica espacial a causa de diverses limitacions relacionades amb les matrius MALDI, incloent la generació d'ions que interfereixen en el rang de masses més baix i la difusió lateral dels compostos. Hem desenvolupat un flux de treball que millora l'adquisició de metabòlits en un instrument MALDI utilitzant un "sputtering" per dipositar una nano-capa d'Au directament sobre el teixit. Això minimitza la interferència dels senyals del "background" alhora que permet resolucions espacials molt altes. S'ha desenvolupat un paquet R per a la visualització d'imatges i processament de les dades MSI, tot això mitjançant una implementació optimitzada per a la gestió de la memòria i la programació concurrent. A més, el programari desenvolupat inclou també un algoritme per a l'alineament de masses que millora la precisió de massa.La imagen por espectrometría de masas (MSI) mapea la distribución espacial de las moléculas en una muestra. Esto permite extraer información metabolòmica espacialmente corralada de una sección de tejido. MSI no se usa ampliamente en la metabolòmica espacial debido a varias limitaciones relacionadas con las matrices MALDI, incluyendo la generación de iones que interfieren en el rango de masas más bajo y la difusión lateral de los compuestos. Hemos desarrollado un flujo de trabajo que mejora la adquisición de metabolitos en un instrumento MALDI utilizando un “sputtering” para depositar una nano-capa de Au directamente sobre el tejido. Esto minimiza la interferencia de las señales del “background” a la vez que permite resoluciones espaciales muy altas. Se ha desarrollado un paquete R para la visualización de imágenes y procesado de los datos MSI, todo ello mediante una implementación optimizada para la gestión de la memoria y la programación concurrente. Además, el software desarrollado incluye también un algoritmo para el alineamiento de masas que mejora la precisión de masa.Mass spectrometry imaging (MSI) maps the spatial distributions of molecules in a sample. This allows extracting spatially-correlated metabolomics information from tissue sections. MSI is not widely used in spatial metabolomics due to several limitations related with MALDI matrices, including the generation of interfering ions and in the low mass range and the lateral compound delocalization. We developed a workflow to improve the acquisition of metabolites using a MALDI instrument. We sputter an Au nano-layer directly onto the tissue section enabling the acquisition of metabolites with minimal interference of background signals and ultra-high spatial resolution. We developed an R package for image visualization and MSI data processing, which is optimized to manage datasets larger than computer’s memory using a mutli-threaded implementation. Moreover, our software includes a label-free mass alignment algorithm for mass accuracy enhancement

An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture