Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual ...identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.
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Collagen quantity and integrity play an important role in understanding diseases such as myelofibrosis (MF). Label-free mid-infrared spectroscopic imaging (MIRSI) has the potential to quantify ...collagen while minimizing the subjective variance observed with conventional histopathology. Infrared (IR) spectroscopy with polarization sensitivity provides chemical information while also estimating tissue dichroism. This can potentially aid MF grading by revealing the structure and orientation of collagen fibers. Simultaneous measurement of collagen structure and biochemical properties can translate clinically into improved diagnosis and enhance our understanding of disease progression. In this paper, we present the first report of polarization-dependent spectroscopic variations in collagen from human bone marrow samples. We build on prior work with animal models and extend it to human clinical biopsies with a practical method for high-resolution chemical and structural imaging of bone marrow on clinical glass slides. This is done using a new polarization-sensitive photothermal mid-infrared spectroscopic imaging scheme that enables sample and source independent polarization control. This technology provides 0.5 µm spatial resolution, enabling the identification of thin (≈1 µm) collagen fibers that were not separable using Fourier Transform Infrared (FT-IR) imaging in the fingerprint region at diffraction-limited resolution ( ≈ 5 µm). Finally, we propose quantitative metrics to identify fiber orientation from discrete band images (amide I and amide II) measured under three polarizations. Previous studies have used a pair of orthogonal polarization measurements, which is insufficient for clinical samples since human bone biopsies contain collagen fibers with multiple orientations. Here, we address this challenge and demonstrate that three polarization measurements are necessary to resolve orientation ambiguity in clinical bone marrow samples. This is also the first study to demonstrate the ability to spectroscopically identify thin collagen fibers (≈1 µm diameter) and their orientations, which is critical for accurate grading of human bone marrow fibrosis.
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Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly ...sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell localization tasks when compared to existing methods on large 3D brain images.
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Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is the most common form of dementia in aged populations. A substantial amount of data demonstrates that chronic ...neuroinflammation can accelerate neurodegenerative pathologies. In AD, chronic neuroinflammation results in the upregulation of cyclooxygenase and increased production of prostaglandin H2, a precursor for many vasoactive prostanoids. While it is well-established that many prostaglandins can modulate the progression of neurodegenerative disorders, the role of prostacyclin (PGI2) in the brain is poorly understood. We have conducted studies to assess the effect of elevated prostacyclin biosynthesis in a mouse model of AD. Upregulated prostacyclin expression significantly worsened multiple measures associated with amyloid-β (Aβ) disease pathologies. Mice overexpressing both Aβ and PGI2 exhibited impaired learning and memory and increased anxiety-like behavior compared with non-transgenic and PGI2 control mice. PGI2 overexpression accelerated the development of Aβ accumulation in the brain and selectively increased the production of soluble Aβ
. PGI2 damaged the microvasculature through alterations in vascular length and branching; Aβ expression exacerbated these effects. Our findings demonstrate that chronic prostacyclin expression plays a novel and unexpected role that hastens the development of the AD phenotype.
High-throughput imaging techniques, such as Knife-Edge Scanning Microscopy (KESM),are capable of acquiring three-dimensional whole-organ images at sub-micrometer resolution. These images are ...challenging to segment since they can exceed several terabytes (TB) in size, requiring extremely fast and fully automated algorithms. Staining techniques are limited to contrast agents that can be applied to large samples and imaged in a single pass. This requires maximizing the number of structures labeled in a single channel, resulting in images that are densely packed with spatial features. In this paper, we propose a three-dimensional approach for locating cells based on iterative voting. Due to the computational complexity of this algorithm, a highly efficient GPU implementation is required to make it practical on large data sets. The proposed algorithm has a limited number of input parameters and is highly parallel.
Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to ...play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.
Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the ...need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.
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Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they ...are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral
and
spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.
Infrared spectroscopy combined with deep learning provide an automated and quantitative alternative to traditional histological examination.
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist’s ability to extract information on diagnosis, prognosis, and therapy ...responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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