scholarly journals Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

2021 ◽  
Author(s):  
Cefa Karabağ ◽  
Martin L. Jones ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Plasma Membrane ◽  
Hela Cells ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Distance Map ◽  
Block Face ◽  
Processing Steps ◽  
Membrane Segmentation ◽  
Instance Segmentation ◽  
Traditional Image

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.

scholarly journals Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

2021 ◽  
Vol 7 (6) ◽  
pp. 93
Author(s):  
Cefa Karabağ ◽  
Martin L. Jones ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Plasma Membrane ◽  
Hela Cells ◽  
Similarity Index ◽  
Ground Truth ◽  
Regions Of Interest ◽  
Distance Map ◽  
Block Face ◽  
Processing Steps ◽  
Instance Segmentation ◽  
Traditional Image

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.

scholarly journals Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3313
Author(s):  
Pierandrea Cancian ◽  
Nina Cortese ◽  
Matteo Donadon ◽  
Marco Di Maio ◽  
Cristiana Soldani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Clinical Practice ◽  
Quantitative Analysis ◽  
Liver Metastasis ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Daily Clinical Practice ◽  
Instance Segmentation

Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools.

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Fault Detection by Using Instance Segmentation

2021 ◽  
Author(s):  
Donglin Zhu ◽  
Lei Li ◽  
Rui Guo ◽  
Shifan Zhan
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Data Interpretation ◽  
Machine Learning Methods ◽  
Segmentation Algorithms ◽  
Direct Recognition ◽  
First Time ◽  
Seismic Images ◽  
Instance Segmentation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

scholarly journals Entry of the Two Infectious Forms of Vaccinia Virus at the Plasma Membane Is Signaling-Dependent for the IMV but Not the EEV

2000 ◽  
Vol 11 (7) ◽  
pp. 2497-2511 ◽  
Author(s):  
Jacomine Krijnse Locker ◽  
Annett Kuehn ◽  
Sibylle Schleich ◽  
Gaby Rutter ◽  
Heinrich Hohenberg ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Protein Kinase C ◽  
Protein Kinase ◽  
Cell Surface ◽  
Vaccinia Virus ◽  
Hela Cells ◽  
Signaling Cascade ◽  
Enveloped Virus ◽  
Kinase C ◽  
Mature Virus

The simpler of the two infectious forms of vaccinia virus, the intracellular mature virus (IMV) is known to infect cells less efficiently than the extracellular enveloped virus (EEV), which is surrounded by an additional, TGN-derived membrane. We show here that when the IMV binds HeLa cells, it activates a signaling cascade that is regulated by the GTPase rac1 and rhoA, ezrin, and both tyrosine and protein kinase C phosphorylation. These cascades are linked to the formation of actin and ezrin containing protrusions at the plasma membrane that seem to be essential for the entry of IMV cores. The identical cores of the EEV also appear to enter at the cell surface, but surprisingly, without the need for signaling and actin/membrane rearrangements. Thus, in addition to its known role in wrapping the IMV and the formation of intracellular actin comets, the membrane of the EEV seems to have evolved the capacity to enter cells silently, without a need for signaling.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 483-492
Author(s):  
D. Gritzner ◽  
J. Ostermann
Keyword(s):  
Time Window ◽  
Domain Adaptation ◽  
Geographical Area ◽  
Model Performance ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
Aerial Images ◽  
Target Domain ◽  
Training Examples

Abstract. Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases.

Ability to organize microtubules in taxol-treated mitotic PtK2 cells goes with the SPN antigen and not with the centrosome

1992 ◽  
Vol 102 (1) ◽  
pp. 91-102 ◽  
Author(s):  
M. Kallajoki ◽  
K. Weber ◽  
M. Osborn
Keyword(s):  
Plasma Membrane ◽  
Hela Cells ◽  
Nuclear Matrix ◽  
Essential Role ◽  
Soluble Form ◽  
Microtubule Nucleation ◽  
Brain Microtubules ◽  
Mitotic Cells

The SPN antigen plays an essential role in mitosis, since microinjection of antibodies causes mitotic arrest. Here we show, by examination of the relative locations of SPN antigen, the centrosomal 5051 antigen and tubulin in normal mitotic, and in taxol-treated mitotic cells, that the SPN antigen is involved in organizing the microtubules of the spindle. The 210 kDa protein defined as SPN antigen relocates from the nuclear matrix to the centrosome at prophase, remains associated with the poles at metaphase and anaphase, and dissociates from the centrosomes in telophase. In taxol-treated mitotic cells, SPN staining shows a striking redistribution while 5051 antigen remains associated with centrosomes. SPN antigen is seen at the plasma membrane end of the rearranged microtubules. SPN antigen is always at the center of the multiple microtubule asters (5 to 20 per cell) induced by taxol, whereas 5051 again remains associated with the centrosomal complex (1 to 2 foci per cell). Microtubule nucleation is associated with the SPN antigen rather than with the 5051 antigen. Microinjection of SPN-3 antibody into taxol-treated mitotic PtK2 cells causes disruption of the asters as judged by tubulin staining of the same cells. Finally, SPN antigen extracted in soluble form from synchronized mitotic HeLa cells binds to, and sediments with, pig brain microtubules stabilized by taxol. This association of SPN antigen with microtubules is partially dissociated by 0.5 M NaCl but not by 5 mM ATP. Thus SPN antigen binds to microtubules in vitro and seems to act as a microtubular minus-end organizer in mitotic cells in vivo.

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