Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F. Claire; Leitinger, Gerd

doi:10.1016/j.jneumeth.2016.02.019

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…The methods include SVM-based algorithms 12 – 16 , Random Forests 12 , 17 – 24 , Conditional Random Fields 22 , and Artificial Neural Networks 25 – 30 ( i.e ., deep learning). These machine learning approaches can be found in popular software packages for connectomics image analysis such as Rhoana 21 and Ilastik 18 , 19 , 31 . In addition to traditional artificial neural network-based approaches, research into fully-convolutional networks for image segmentation related to microscopy produced the U-Net 32 architecture.…”

Section: Introductionmentioning

confidence: 99%

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Shahbazi

Kinnison

Vescovi

et al. 2018

Sci Rep

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Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

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Section: Introductionmentioning

confidence: 99%

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Shahbazi

Kinnison

Vescovi

et al. 2018

Sci Rep

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“…The brain from an adult locust (Locusta migratoria) was sacrificed by ice, dissected in cold saline and placed in 2% paraformaldehyde, 2.5% glutaraldehyde in 0.1M sodium cacodylate buffer. It was processed with an adapted version of the Wilke et al (2013) protocol described in Wernitznig et al (2016). The main differences to the above protocol are the use of reduced osmium (1%) and a shorter time in uranyl acetate but at 60°C.…”

Section: Tissue Preparation For Sbf-semmentioning

confidence: 99%

A guide to analysis and reconstruction of serial block face scanning electron microscopy data

Cocks

Taggart

Rind

et al. 2018

Journal of Microscopy

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SummarySerial block face scanning electron microscopy (SBF‐SEM) is a relatively new technique that allows the acquisition of serially sectioned, imaged and digitally aligned ultrastructural data. There is a wealth of information that can be obtained from the resulting image stacks but this presents a new challenge for researchers – how to computationally analyse and make best use of the large datasets produced. One approach is to reconstruct structures and features of interest in 3D. However, the software programmes can appear overwhelming, time‐consuming and not intuitive for those new to image analysis. There are a limited number of published articles that provide sufficient detail on how to do this type of reconstruction. Therefore, the aim of this paper is to provide a detailed step‐by‐step protocol, accompanied by tutorial videos, for several types of analysis programmes that can be used on raw SBF‐SEM data, although there are more options available than can be covered here. To showcase the programmes, datasets of skeletal muscle from foetal and adult guinea pigs are initially used with procedures subsequently applied to guinea pig cardiac tissue and locust brain. The tissue is processed using the heavy metal protocol developed specifically for SBF‐SEM. Trimmed resin blocks are placed into a Zeiss Sigma SEM incorporating the Gatan 3View and the resulting image stacks are analysed in three different programmes, Fiji, Amira and MIB, using a range of tools available for segmentation. The results from the image analysis comparison show that the analysis tools are often more suited to a particular type of structure. For example, larger structures, such as nuclei and cells, can be segmented using interpolation, which speeds up analysis; single contrast structures, such as the nucleolus, can be segmented using the contrast‐based thresholding tools. Knowing the nature of the tissue and its specific structures (complexity, contrast, if there are distinct membranes, size) will help to determine the best method for reconstruction and thus maximize informative output from valuable tissue.

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“…It is important that we consider the use of SBF-SEM and FIB-SEM in combination with design-based stereology to advance our understanding of the ultrastructure of nervous tissue (Waworuntu et al, 2016 ). Ongoing developments in the capture of high resolution images and in the methods of reconstruction and optimization of images, will ensure that in the near future this is a very efficient process (Bellesi et al, 2015 ; Borrett and Hughes, 2016 ; Nguyen et al, 2016 ; Wernitznig et al, 2016 ).…”

Section: Total Number—the Futurementioning

confidence: 99%

Total Number Is Important: Using the Disector Method in Design-Based Stereology to Understand the Structure of the Rodent Brain

Napper

2018

Front. Neuroanat.

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The advantages of using design-based stereology in the collection of quantitative data, have been highlighted, in numerous publications, since the description of the disector method by Sterio (1984). This review article discusses the importance of total number derived with the disector method, as a key variable that must continue to be used to understand the rodent brain and that such data can be used to develop quantitative networks of the brain. The review article will highlight the huge impact total number has had on our understanding of the rodent brain and it will suggest that neuroscientists need to be aware of the increasing number of studies where density, not total number, is the quantitative measure used. It will emphasize that density can result in data that is misleading, most often in an unknown direction, and that we run the risk of this type of data being accepted into the collective neuroscience knowledge database. It will also suggest that design-based stereology using the disector method, can be used alongside recent developments in electron microscopy, such as serial block-face scanning electron microscopy (SEM), to obtain total number data very efficiently at the ultrastructural level. Throughout the article total number is discussed as a key parameter in understanding the micro-networks of the rodent brain as they can be represented as both anatomical and quantitative networks.

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Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

Cited by 9 publications

References 46 publications

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

A guide to analysis and reconstruction of serial block face scanning electron microscopy data

Total Number Is Important: Using the Disector Method in Design-Based Stereology to Understand the Structure of the Rodent Brain

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