Large scale three-dimensional reconstruction of an entire Caenorhabditis elegans larva using AutoCUTS-SEM

Li, Xixia; Ji, Gang; Chen, Xi; Ding, W. M.; Sun, Lei; Xu, Wei; Han, Hua; Sun, Fei

doi:10.1016/j.jsb.2017.09.010

Cited by 30 publications

(39 citation statements)

References 37 publications

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“…Because the shape of mitochondria is sometimes irregular, manual annotations near mitochondria borders are not always accurate, which might influence the precision of the evaluation. Motivated by Li et al ( 2017b ); Tasel et al ( 2016 ), we defined that a predicted mitochondria is considered a TP only if the voxel-wise overlap between the prediction and corresponding ground truth reaches at least 70%. For the sake of completeness, we also conducted several experiments by considering different voxel-wise overlapping thresholds for TP on both datasets.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned above, the data imaged with the ATUM-SEM method were unregistered. The image registration method adopted (Li et al, 2017b ) for serial sections of biological tissue was divided into three parts: (1) searching for correspondences between adjacent sections; (2) displacement calculations for the identified correspondences; and (3) warping the image tiles based on the new position of these correspondences. For correspondence searching, we adopted the SIFT-flow algorithm (Liu et al, 2008 ) to search for correspondences between adjacent sections by extracting equally distributed grid points from the well-aligned adjacent sections.…”

Section: Methodsmentioning

confidence: 99%

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Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Xiao

Chen

et al. 2018

Front. Neuroanat.

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Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Xiao

Chen

et al. 2018

Front. Neuroanat.

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“…An alternative approach using the ATUMtome machine for automatic section collection from neuronal samples was recently demonstrated (Kasthuri et al, 2015). Likewise, an alternative AutoCUTS method was recently used to collect sections of C. elegans larvae (Li et al, 2017). However, this time-consuming and more expensive strategy is only worthwhile when there is a requirement for thousands of sections, as is often needed for brain samples.…”

Section: Discussionmentioning

confidence: 99%

A targeted 3D EM and correlative microscopy method using SEM array tomography

et al. 2018

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Using electron microscopy to localize rare cellular events or structures in complex tissue is challenging. Correlative light and electron microscopy procedures have been developed to link fluorescent protein expression with ultrastructural resolution. Here, we present an optimized scanning electron microscopy (SEM) workflow for volumetric array tomography for asymmetric samples and model organisms (, ,). We modified a diamond knife to simplify serial section array acquisition with minimal artifacts. After array acquisition, the arrays were transferred to a glass coverslip or silicon wafer support. Using light microscopy, the arrays were screened rapidly for initial recognition of global anatomical features (organs or body traits). Then, using SEM, an in-depth study of the cells and/or organs of interest was performed. Our manual and automatic data acquisition strategies make 3D data acquisition and correlation simpler and more precise than alternative methods. This method can be used to address questions in cell and developmental biology that require the efficient identification of a labeled cell or organelle.

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“…The datasets were collected from a water bath using a custom designed tape-collection conveyor belt in the Institute of Neuroscience, Chinese Academy of Sciences, where several slices with thicknesses of more or less 50 nm were cut automatically. Next, these sections were imaged through SEM (Zeiss Supra55) in the Institute of Automation, Chinese Academy of Sciences, where the pixel size was set at 2 nm and the dwell time was set at 2 μ s. Since the datasets acquired by the ATUM-SEM technique were unregistered, the image registration method applied in [ 17 ] was adopted in this paper. After registration, two ATUM-SEM datasets are used to construct the corresponding databases for mitochondria and synapses, respectively.…”

Section: Methodsmentioning

confidence: 99%

A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images

Liu

Xiao

et al. 2018

BioData Mining

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BackgroundIt is becoming increasingly clear that the quantification of mitochondria and synapses is of great significance to understand the function of biological nervous systems. Electron microscopy (EM), with the necessary resolution in three directions, is the only available imaging method to look closely into these issues. Therefore, estimating the number of mitochondria and synapses from the serial EM images is coming into prominence. Since previous studies have achieved preferable 2D segmentation performance, it holds great promise to obtain the 3D connection relationship from the 2D segmentation results.ResultsIn this paper, we improve upon Matlab’s function bwconncomp and propose a fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images. To benchmark the performance of the proposed method, two EM datasets with the annotated ground truth are produced for mitochondria and synapses, respectively. Experimental results show that the proposed method can achieve the preferable connection performance that closely matches the ground truth. Moreover, it greatly reduces the computational burden and alleviates the memory requirements compared with the function bwconncomp.ConclusionsThe proposed method can be deemed as an effective strategy to obtain the 3D connection relationship from serial mitochondria and synapse segmentations. It is helpful to accurately and quickly quantify the statistics of the numbers, volumes, surface areas, and lengths, which will greatly facilitate the data analysis of neurobiology research.

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Large scale three-dimensional reconstruction of an entire Caenorhabditis elegans larva using AutoCUTS-SEM

Cited by 30 publications

References 37 publications

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

A targeted 3D EM and correlative microscopy method using SEM array tomography

A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images

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