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Duque-Vazquez EF, Sanchez-Yanez RE, Saldaña-Robles N, León-Galván MF, Cepeda-Negrete J. HeLa cell segmentation using digital image processing. Heliyon 2024; 10:e26520. [PMID: 38434298 PMCID: PMC10907640 DOI: 10.1016/j.heliyon.2024.e26520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/28/2023] [Accepted: 02/14/2024] [Indexed: 03/05/2024] Open
Abstract
Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.
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Affiliation(s)
- Edgar F Duque-Vazquez
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Raul E Sanchez-Yanez
- Universidad de Guanajuato DICIS, Carretera Salamanca - Valle de Santiago km 3.5 + 1.8 Comunidad de Palo Blanco, Salamanca, 36885 Guanajuato, Mexico
| | - Noe Saldaña-Robles
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Ma Fabiola León-Galván
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Jonathan Cepeda-Negrete
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
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Pagano L, Thibault G, Bousselham W, Riesterer JL, Song X, Gray JW. Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations. FRONTIERS IN BIOINFORMATICS 2023; 3:1308707. [PMID: 38162122 PMCID: PMC10757843 DOI: 10.3389/fbinf.2023.1308707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024] Open
Abstract
Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.
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Affiliation(s)
- Lucas Pagano
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Walid Bousselham
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Jessica L. Riesterer
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Xubo Song
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
- Department of Medical Informatics and Clinical Epidemiology at Oregon Health and Science University, Portland, OR, United States
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
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3
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Pagano L, Thibault G, Bousselham W, Riesterer JL, Song X, Gray JW. Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.563998. [PMID: 37961180 PMCID: PMC10635003 DOI: 10.1101/2023.10.30.563998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.
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Affiliation(s)
- Lucas Pagano
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Walid Bousselham
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Jessica L. Riesterer
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Xubo Song
- Department of Medical Informatics and Clinical Epidemiology at Oregon Health and Science University, Portland, OR USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
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Parada-Cabaleiro E, Batliner A, Schmitt M, Schedl M, Costantini G, Schuller B. Perception and classification of emotions in nonsense speech: Humans versus machines. PLoS One 2023; 18:e0281079. [PMID: 36716307 PMCID: PMC9886254 DOI: 10.1371/journal.pone.0281079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/13/2023] [Indexed: 02/01/2023] Open
Abstract
This article contributes to a more adequate modelling of emotions encoded in speech, by addressing four fallacies prevalent in traditional affective computing: First, studies concentrate on few emotions and disregard all other ones ('closed world'). Second, studies use clean (lab) data or real-life ones but do not compare clean and noisy data in a comparable setting ('clean world'). Third, machine learning approaches need large amounts of data; however, their performance has not yet been assessed by systematically comparing different approaches and different sizes of databases ('small world'). Fourth, although human annotations of emotion constitute the basis for automatic classification, human perception and machine classification have not yet been compared on a strict basis ('one world'). Finally, we deal with the intrinsic ambiguities of emotions by interpreting the confusions between categories ('fuzzy world'). We use acted nonsense speech from the GEMEP corpus, emotional 'distractors' as categories not entailed in the test set, real-life noises that mask the clear recordings, and different sizes of the training set for machine learning. We show that machine learning based on state-of-the-art feature representations (wav2vec2) is able to mirror the main emotional categories ('pillars') present in perceptual emotional constellations even in degradated acoustic conditions.
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Affiliation(s)
- Emilia Parada-Cabaleiro
- Institute of Computational Perception, Johannes Kepler University Linz, Linz, Austria
- Human-centered AI Group, Linz Institute of Technology (LIT), Linz, Austria
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Anton Batliner
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Maximilian Schmitt
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Markus Schedl
- Institute of Computational Perception, Johannes Kepler University Linz, Linz, Austria
- Human-centered AI Group, Linz Institute of Technology (LIT), Linz, Austria
| | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Björn Schuller
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM—Group on Language, Audio & Music, Imperial College London, London, United Kindom
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Weighted average ensemble-based semantic segmentation in biological electron microscopy images. Histochem Cell Biol 2022; 158:447-462. [DOI: 10.1007/s00418-022-02148-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/08/2022] [Indexed: 12/16/2022]
Abstract
AbstractSemantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.
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Karabağ C, Jones ML, Reyes-Aldasoro CC. Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells. J Imaging 2021; 7:93. [PMID: 39080881 PMCID: PMC8321355 DOI: 10.3390/jimaging7060093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 01/10/2023] Open
Abstract
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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Affiliation(s)
- Cefa Karabağ
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK;
| | - Martin L. Jones
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London NW1 1AT, UK;
| | - Constantino Carlos Reyes-Aldasoro
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK;
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Conrad R, Narayan K. CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning. eLife 2021; 10:e65894. [PMID: 33830015 PMCID: PMC8032397 DOI: 10.7554/elife.65894] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/13/2021] [Indexed: 01/03/2023] Open
Abstract
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pre-training on currently available EM datasets is computationally expensive and shows little value for unseen biological contexts, as these datasets are large and homogeneous. To address this issue, we present CEM500K, a nimble 25 GB dataset of 0.5 × 106 unique 2D cellular EM images curated from nearly 600 three-dimensional (3D) and 10,000 two-dimensional (2D) images from >100 unrelated imaging projects. We show that models pre-trained on CEM500K learn features that are biologically relevant and resilient to meaningful image augmentations. Critically, we evaluate transfer learning from these pre-trained models on six publicly available and one newly derived benchmark segmentation task and report state-of-the-art results on each. We release the CEM500K dataset, pre-trained models and curation pipeline for model building and further expansion by the EM community. Data and code are available at https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10592/ and https://git.io/JLLTz.
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Affiliation(s)
- Ryan Conrad
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of HealthBethesdaUnited States
- Cancer Research Technology Program, Frederick National Laboratory for Cancer ResearchFrederickUnited States
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of HealthBethesdaUnited States
- Cancer Research Technology Program, Frederick National Laboratory for Cancer ResearchFrederickUnited States
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