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

Abstract

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.

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

“…The initial step of the image-processing algorithm [73] filtered the images with a low-pass filter with a Gaussian kernel with size h = 7 and standard deviation σ = 2 to remove high frequencies (Fig. 3a).…”

“…The algorithms were pre-trained with ImageNet and then fine-tuned with training data prepared for this work. These were then compared with a traditional image processing algorithm [73]. The image processing algorithm followed a pipeline of traditional tasks: low-pass filtering, edge detection, dilation, generation of super-pixels, distance transforms, mathematical morphology and post-processing to segment automatically the nuclear envelope and background of HeLa cells.…”

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

“…Details of the cell preparation have been published previously [14], but briefly, the data set consisted of EM images of HeLa cells. HeLa cells were prepared and embedded in Durcupan resin following the method of the National Centre for Microscopy and Imaging Research (NCMIR) [7].…”

“…The methodology for the automated segmentation algorithm has been published before [14], but for completeness is summarised in this section.…”

“…The volumetric NEs of the cells of the two populations are compared from a geometrical point of view. The NE is automatically segmented and modelled against a spheroid with the algorithms described in [14]. The modelled NE is converted to a 2D surface, from which a series of metrics are extracted.…”