421 research outputs found
Globally optimal stitching of tiled 3D microscopic image acquisitions
Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks
As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets
Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections
As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets
Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
Structural and mechanical differences between cancerous and healthy tissue
give rise to variations in macroscopic properties such as visual appearance and
elastic modulus that show promise as signatures for early cancer detection.
Atomic force microscopy (AFM) has been used to measure significant differences
in stiffness between cancerous and healthy cells owing to its high force
sensitivity and spatial resolution, however due to absorption and scattering of
light, it is often challenging to accurately locate where AFM measurements have
been made on a bulk tissue sample. In this paper we describe an image
registration method that localizes AFM elastic stiffness measurements with
high-resolution images of haematoxylin and eosin (H\&E)-stained tissue to
within 1.5 microns. Color RGB images are segmented into three structure types
(lumen, cells and stroma) by a neural network classifier trained on
ground-truth pixel data obtained through k-means clustering in HSV color space.
Using the localized stiffness maps and corresponding structural information, a
whole-sample stiffness map is generated with a region matching and
interpolation algorithm that associates similar structures with measured
stiffness values. We present results showing significant differences in
stiffness between healthy and cancerous liver tissue and discuss potential
applications of this technique.Comment: Accepted at MICCAI201
Virtual Microscopy with Extended Depth of Field
In this paper, we describe a virtual microscope system, based on JPEG 2000, which utilizes extended depth of field (EDF) imaging. Through a series of observer trials we show that EDF imaging improves both the local image quality of individual fields of view (FOV) and the accuracy with which the FOVs can be mosaiced (stitched) together. In addition, we estimate the required bit rate to adequately render a set of histology and cytology specimens at a quality suitable for on-line learning and collaboration. We show that, using JPEG 2000, we can efficiently represent high-quality, high-resolution colour images of microscopic specimens with less than 1 bit per pixel
Optimized Protocol for Imaging Cleared Neural Tissues Using Light Microscopy
Understanding physical and chemical processes at an organismal scale is a fundamental goal in biology. While science is adept at explaining biological phenomena at both molecular and cellular levels, understanding how these processes translate to organismal functions remains a challenging problem. This issue is particularly significant for the nervous system where cell signaling and synaptic activities function in the context of broad neural networks. Recent progress in tissue clearing technologies lessens the barriers that previously prevented the study of large tissue samples while maintaining molecular and cellular resolution. While these new methods open vast opportunities and exciting new questions, the logistics of analyzing cellular processes in intact tissue have to be carefully considered. In this protocol, we outline a procedure to rapidly image intact brain tissue up to thousands of cubic millimeters. This experimental pipeline involves three steps: tissue clearing, tissue imaging, and data analysis. In an attempt to streamline the process for researchers entering this field, we address important considerations for each of these stages and describe an integrated solution to image intact biological tissues. Hopefully, this optimized protocol will lower the barrier of implementing high-resolution tissue imaging and facilitate the investigations of mesoscale questions at molecular and cellular resolution
A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces
Optical instruments are widely used for precision surface measurement. However, the dynamic range of optical instruments, in terms of measurement area and resolution, is limited by the characteristics of the imaging and the detection systems. If a large area with a high resolution is required, multiple measurements need to be conducted and the resulting datasets needs to be stitched together. Traditional stitching methods use six degrees of freedom for the registration of the overlapped regions, which can result in high computational complexity. Moreover, measurement error increases with increasing measurement data. In this paper, a stitching method, based on a Gaussian process, image registration and edge intensity data fusion, is presented. Firstly, the stitched datasets are modelled by using a Gaussian process so as to determine the mean of each stitched tile. Secondly, the datasets are projected to a base plane. In this way, the three-dimensional datasets are transformed to two-dimensional (2D) images. The images are registered by using an (x, y) translation to simplify the complexity. By using a high precision linear stage that is integral to the measurement instrument, the rotational error becomes insignificant and the cumulative rotational error can be eliminated. The translational error can be compensated by the image registration process. The z direction registration is performed by a least-squares error algorithm and the (x, y, z) translational information is determined. Finally, the overlapped regions of the measurement datasets are fused together by the edge intensity data fusion method. As a result, a large measurement area with a high resolution is obtained. A simulated and an actual measurement with a coherence scanning interferometer have been conducted to verify the proposed method. The stitching result shows that the proposed method is technically feasible for large area surface measurement
Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21
Published in final edited form as:
Brain Struct Funct. 2019 January ; 224(1): 351–362. doi:10.1007/s00429-018-1777-z.Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.Accepted manuscrip
- …