Block Face Scanning Electron Microscopy of Fluorescently Labeled Axons Without Using Near Infra-Red Branding

In this article, we describe the method that allows fluorescently tagged structures such as axons to be targeted for electron microscopy (EM) analysis without the need to convert their labels into electron dense stains, introduce any fiducial marks, or image large volumes at high resolution. We optimally preserve and stain the brain tissue for ultrastructural analysis and use natural landmarks, such as cell bodies and blood vessels, to locate neurites that had been imaged previously using confocal microscopy. The method relies on low and high magnification views taken with the light microscope, after fixation, to capture information of the tissue structure that can later be used to pinpoint the position of structures of interest in serial EM images. The examples shown here are td Tomato expressing cortico-thalamic axons in the posteromedial nucleus of the mouse thalamus, imaged in fixed tissue with confocal microscopy, and subsequently visualized with serial block-face EM (SBEM) and reconstructed into 3D models for analysis.

NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks

Corrado Cali, Pascal Fua, Anne Alison Jorstad, Graham Knott, Biagio Nigro, Marta Wawrzyniak

Serial electron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

Humana Press2015

Software Tools for Handling Magnetically Collected Ultra-thin Sections for Microscopy

Jelena Banjac

This report presents the software tools that will be available online to help users accurately segment ultra-thin sections of brain tissue in a large image to determine the section’s coordinates. In order to predict these coordinates, the goal was to use a state-of-art object instance segmentation framework called Masked Region-based Convolutional Neural Network (Masked R-CNN) on the dataset containing sections of brain tissue in the light microscopy images. The predicted coordinates of the sections will later be used for automated image acquisition in the high-resolution electron microscope. We will demonstrate that Masked R-CNN can be used to perform highly effective and efficient automatic segmentation of microscopy images containing the sections. The machine learning pipeline used will be explained in detail. In addition, we will show the results of how different parameters and settings of this pipeline were changing the performance. This semester's project was done in collaboration with the Center for Interdisciplinary Electron Microscopy (CIME) lab and Machine Learning and Optimization (MLO) lab at EPFL.

2019

Application of CMOS image sensors in optical sectioning and polarization microscopy

Jelena Mitic

In this thesis new imaging approaches for optical microscopy are proposed and studied. They are based on the use of dynamic structured illumination in combination with a demodulation detection concept employing CMOS image detectors. Two particular implementations are suggested: real-time optical sectioning microscopy and whole field quantitative polarization microscopy. Optical sectioning, i.e depth resolved imaging, allows observation of thin sections in volumetric samples. In its classical configuration the wide-field microscope does not allow optical sectioning of the investigated objects. In this thesis the optical sectioning in a wide-field microscope is achieved by illuminating the sample with a continuously moving single spatial frequency pattern, which temporarily modulates the signal obtained on the photodetector. Only the object parts that are in the focus have a good contrast and consequently generate stronger signal on the detector. The optically sectioned images are obtained employing a CMOS image detector, which demodulates the time-dependent component of the image. Two different systems for real-time acquisition of optically sectioned images are proposed. The first one is based on a specially designed smart-pixel-detector-array (SPDA), which allows performing the signal processing by an electronic circuit integrated to each pixel of the sensor. The second system utilizes a commercial CMOS image sensor. Here, the images are treated by a digital signal processor (DSP) integrated into the camera (iMVS-155). Both approaches provide real-time acquisition of optically sectioned images. The proof of principle for the new method is demonstrated by imaging artificial three-dimensional reflective samples. The optical sectioning imaging of biological samples in bright-field mode and in fluorescence mode is also demonstrated. The optical sectioning performances of the studied systems are similar to that of the confocal microscope. However the new method has some advantages over the classical confocal system: e.g. the difficulties with the alignment and the post-processing inherent to the confocal system are avoided. The theoretical framework presented in the thesis describes the image formation in structured illumination for both reflective and fluorescent samples. The influence of longitudinal chromatic aberration and spherical aberration caused by specimen induced index mismatch on depth discrimination property are studied. In extension to the work related to optically sectioned microscopy, the quantitative polarization microscopy imaging method is proposed as a new application for CMOS detectors. The polarization state of the illuminating light is dynamically changed and the demodulation detection approach is employed to extract the retardance and azimuth of the birefringent sample in real-time; for a whole field of view. Examples of polarization sensitive measurements for different samples are presented. The theoretical analysis is performed using the Jones formalism. Finally, the potentials and limitations of the CMOS detectors for applications in optical microscopy are discussed. Thanks to constant advancements in CMOS technology, the acquisition speed, resolution and sensitivity of new CMOS detectors generations have been improved. This will allow adapting the methods proposed in this thesis for investigations of fast dynamic process where high temporal and spatial resolution is required.

EPFL2004

Application of CMOS image sensors in optical sectioning and polarization microscopy

Jelena Mitic

EPFL2004