Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images

Karabağ, Cefa; Jones, Martin L.; Peddie, Christopher J.; Weston, Anne; Collinson, Lucy M.; Reyes-Aldasoro, Constantino Carlos

doi:10.1007/978-3-319-95921-4_23

Cited by 4 publications

(8 citation statements)

References 32 publications

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“…Notice that both the algorithm and GT detected disjoint regions. A preliminary work approach described in [50] assumed that there was only one nuclear region in each image, which was designated as the nucleus, and any disjoint regions which could or not be part of the nucleus were ignored. That approach worked well for cells with a smooth near-spherical nuclear morphology.…”

Section: Automatic Segmentation Of the Nuclear Envelope Algorithmmentioning

confidence: 99%

Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

et al. 2019

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This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calculating edges, which are then used to generate superpixels. The superpixels are morphologically processed and those that correspond to the nuclear region are selected through the analysis of size, position, and correspondence with regions detected in neighbouring slices. The nuclear envelope is segmented from the nuclear region. The three-dimensional segmented nuclear envelope is then modelled against a spheroid to create a two-dimensional (2D) surface. The 2D surface summarises the complex 3D shape of the nuclear envelope and allows the extraction of metrics that may be relevant to characterise the nature of cells. The algorithm was developed and validated on a single cell and tested in six separate cells, each with 300 slices of 2000 × 2000 pixels. Ground truth was available for two of these cells, i.e., 600 hand-segmented slices. The accuracy of the algorithm was evaluated with two similarity metrics: Jaccard Similarity Index and Mean Hausdorff distance. Jaccard values of the first/second segmentation were 93%/90% for the whole cell, and 98%/94% between slices 75 and 225, as the central slices of the nucleus are more regular than those on the extremes. Mean Hausdorff distances were 9/17 pixels for the whole cells and 4/13 pixels for central slices. One slice was processed in approximately 8 s and a whole cell in 40 min. The algorithm outperformed active contours in both accuracy and time.

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Section: Automatic Segmentation Of the Nuclear Envelope Algorithmmentioning

confidence: 99%

Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

et al. 2019

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“…Details of the acquisition have been published previously [10], but briefly, the data sets consisted of EM images of cancerous HeLa cells. The method of the National Centre for Microscopy and Imaging Research (NCMIR) [11] was followed and wild type HeLa cells were prepared and embedded in Durcupan resin.…”

Section: Hela Cells Preparation and Acquisitionmentioning

confidence: 99%

“…In this work, an automatic, unsupervised algorithm to segment the NE of HeLa cells is described. The algorithm presented exploits intensity variations of the structures of HeLa cells following [10]. Preliminary work segmented only one cell and, as an extension, this work now segments 7 different cells with disconnected regions which is the main contribution and some metrics were extracted from the surfaces by modelling the 3D surface of the NE against a 3D ellipsoid.…”

Section: Introductionmentioning

confidence: 99%

Segmentation And Modelling of Helanuclear Envelope

Karabağ

Jones

Peddie

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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This paper describes an algorithm to segment the 3D nuclear envelope of HeLa cancer cells from electron microscopy images and model the volumetric shape of the nuclear envelope against an ellipsoid. The algorithm was trained on a single cell and then tested in six separate cells. To assess the algorithm, Jaccard similarity index and Hausdorff distance against a manually-delineated gold standard were calculated on two cells. The mean Jaccard value and Hausdorff distance that the segmentation achieved for central slices were 98% and 4 pixels for the first cell and 94% and 13 pixels for the second cell and outperformed segmentation with active contours. The modelling projects a 3D to a 2D surface that summarises the complexity of the shape in an intuitive result. Measurements extracted from the modelled surface may be useful to correlate shape with biological characteristics. The algorithm is unsupervised, fully automatic, fast and processes one image in less than 10 seconds.

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“…The NE was segmented following the framework described in [22], which is available open-source as Matlab code (https://github.com/reyesaldasoro/HeLa-Cell-Segmentation). The framework exploited the darker intensity of the NE as compared with the cytoplasm and the nucleoplasm by Canny edge detection.…”

Section: Automatic Segmentation Of the Nuclear Envelopementioning

confidence: 99%

“…In this paper, a framework to analyse the NE of HeLa cells is presented. The framework extends an automatic segmentation of the NE [22] and models the NE as a 2D surface against a 3D ellipsoid. Distances from the NE to the ellipsoid and the local variation of the distances are calculated with the expectation that they can allow the identification of different biological processes.…”

Section: Introductionmentioning

confidence: 99%

Modelling the nuclear envelope ofHeLacells

Karabağ

Jones

Peddie

et al. 2018

Preprint

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This paper describes a framework for the automatic segmentation of the nuclear envelope of cancerous HeLa cells and the modelling of the volumetric shape against an ellipsoid. The framework is automatic and unsupervised and reported a Jaccard Similarity Index of 0.968 against a manual segmentation. The modelling of the surface provides a visual display of the variations, both smooth and rugged over the surface, and measurements can be extracted with the expectation that they can correlate with the biological characteristics of the cells.

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Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Cited by 4 publications

References 32 publications

Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

Segmentation And Modelling of Helanuclear Envelope

Modelling the nuclear envelope ofHeLacells

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