Automatic Detection and Segmentation of Mitochondria from SEM Images using Deep Neural Network

Liu, Jing; Li, Weifu; Xiao, Chen; Hong, Bei; Xie, Qiwei; Han, Hua

doi:10.1109/embc.2018.8512393

Cited by 19 publications

(15 citation statements)

References 13 publications

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“…Cireşan [59]et al applied deep learning architectures to detect membrane neuronal and mitosis detection in breast cancer [60]. Within EM studies, deep learning has been applied to analyse mitochondria [61,62], synapses [63] and proteins [64].…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

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The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures:

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Section: Introductionmentioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

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“…The segmentation, identification and analysis of EM cellular images can be performed through manual processes [ 52 , 53 , 54 ], which can be distributed as citizen science where an army of non-experts [ 55 , 56 ] are recruited to provide non-expert human annotation, segmentation or classification through web-based interfaces (e.g., (Accessed on 28 May 2021)) [ 57 ]. Alternatively, computational approaches with traditional algorithms or deep learning approaches have been proposed to detect neuronal membrane and for mitosis detection in breast cancer [ 58 ], mitochondria [ 59 , 60 ], synapses [ 61 ] and proteins [ 62 ]. In particular, the segmentation of the plasma membrane of HeLa cells has been attempted either manually [ 63 , 64 , 65 , 66 ] or at a much lower resolution [ 67 ] (e.g., pixel resolutions around 312 nm, whilst in this work, 10 nm), which constitute a completely different problem.…”

Section: Introductionmentioning

confidence: 99%

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

2021

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In this work, an unsupervised volumetric semantic instance segmentation of the plasma membrane of HeLa cells as observed with serial block face scanning electron microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8192 × 8192 pixels each. The background was used to create a distance map, which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., the largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2000 × 2000 × 300 voxels that were treated as cell instances. Then, for each of these volumes, the nucleus was segmented, and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions of interest previously selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and the Jaccard similarity Index (JI): nucleus: JI =0.9665, AC =0.9975, cell including nucleus JI =0.8711, AC =0.9655, cell excluding nucleus JI =0.8094, AC =0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background. In samples with tightly packed cells, this may not be available. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data were released openly through GitHub, Zenodo and EMPIAR.

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“…https://www.zooniverse.org/projects/h-spiers/etch-a-cell) [44]. Alternatively, computational approaches with traditional algorithms or deep learning approaches have been proposed to detect membrane neuronal and mitosis detection in breast cancer [45], mitochondria [46,47], synapses [48] and proteins [49]. Besides the well-known limitations of deep learning architectures, of significant computational power, large amount of training data and problems with unrelated datasets which show little value for unseen biological situations [50][51][52][53][54][55], the resolution of the EM data sets can enable or restrict their use for specific purposes.…”

Section: Introductionmentioning

confidence: 99%

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

Karabağ

Jones

Reyes-Aldasoro

2021

Preprint

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In this work, the unsupervised volumetric semantic segmentation of the plasma membrane of HeLa cells as observed with Serial Block Face Scanning Electron Microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8, 192 x 8, 192 pixels each. The background was used to create a distance map which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2, 000 x 2, 000 x 300 voxels that were treated as cell instances. Then, for each of these volumes the nucleus was segmented and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and Jaccard Index (JI): Nucleus: JI = 0.9665, AC= 0.9975, Cell and Nucleus JI = 0.8711, AC = 0.9655, Cell only JI = 0.8094, AC = 0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background, as in cases of tightly packed cells. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data are released openly through GitHub, Zenodo and EMPIAR.

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Automatic Detection and Segmentation of Mitochondria from SEM Images using Deep Neural Network

Cited by 19 publications

References 13 publications

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

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