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Article ; Online: Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Pagano, Lucas / Thibault, Guillaume / Bousselham, Walid / Riesterer, Jessica L / Song, Xubo / Gray, Joe W

Frontiers in bioinformatics

2023 Volume 3, Page(s) 1308707

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 ... ...

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.
Language	English
Publishing date	2023-12-15
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2023.1308707
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Pagano, Lucas / Thibault, Guillaume / Bousselham, Walid / Riesterer, Jessica L / Song, Xubo / Gray, Joe W

bioRxiv : the preprint server for biology

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 ... ...

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.
Language	English
Publishing date	2023-11-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.30.563998
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Article: Automated large volume sample preparation for vEM.

Stempinski, Erin S / Pagano, Lucas / Riesterer, Jessica L / Adamou, Steven K / Thibault, Guillaume / Song, Xubo / Chang, Young Hwan / López, Claudia S

Methods in cell biology

2023 Volume 177, Page(s) 1–32

Abstract: New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. ... ...

Abstract	New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. Moreover, sample preparation methods are continuously being modified by investigators to improve final sample quality, increase electron density, combine imaging technologies, and minimize the introduction of artifacts into specimens under study. There are a variety of conventional bench protocols that a researcher can utilize, though most of these protocols require several days. In this work, we describe the utilization of an automated specimen processor, the mPrep™ ASP-2000™, to prepare samples for vEM that are compatible with focused ion beam scanning electron microscopy (FIB-SEM), serial block face scanning electron microscopy (SBF-SEM), and array tomography (AT). The protocols described here aimed for methods that are completed in a much shorter period of time while minimizing the exposure of the operator to hazardous and toxic chemicals and improving the reproducibility of the specimens' heavy metal staining, all without compromising the quality of the data acquired using backscattered electrons during SEM imaging. As a control, we have included a widely used sample bench protocol and have utilized it as a comparator for image quality analysis, both qualitatively and using image quality analysis metrics.
MeSH term(s)	Microscopy, Electron, Scanning ; Artificial Intelligence ; Reproducibility of Results ; Imaging, Three-Dimensional/methods ; Volume Electron Microscopy
Language	English
Publishing date	2023-05-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	0091-679X
ISSN	0091-679X
DOI	10.1016/bs.mcb.2023.01.009
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Article ; Online: Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.

Machireddy, Archana / Thibault, Guillaume / Loftis, Kevin G / Stoltz, Kevin / Bueno, Cecilia E / Smith, Hannah R / Riesterer, Jessica L / Gray, Joe W / Song, Xubo

Frontiers in bioinformatics

2023 Volume 3, Page(s) 1308708

Abstract: Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. ... ...

Abstract	Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.
Language	English
Publishing date	2023-12-15
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2023.1308708
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images.

Giannios, Katya / Chaurasia, Abhishek / Bueno, Cecilia / Riesterer, Jessica L / Pagano, Lucas / Lo, Terence P / Thibault, Guillaume / Gray, Joe W / Song, Xubo / DeLaRosa, Bambi

bioRxiv : the preprint server for biology

2023

Abstract: Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and ... ...

Abstract	Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the model's generalizibality. This underscores the models' capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.
Language	English
Publishing date	2023-11-27
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.27.568889
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Large-Scale Electron Microscopy to Find Nanoscale Detail in Cancer.

Riesterer, Jessica L / Bueno, Cecilia / Stempinski, Erin S / Adamou, Steven K / López, Claudia S / Thibault, Guillaume / Pagano, Lucas / Grieco, Joseph / Olson, Samuel / Machireddy, Archana / Chang, Young Hwan / Song, Xubo / Gray, Joe W

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

2023 Volume 29, Issue 29 Suppl 1, Page(s) 1078–1079

Language	English
Publishing date	2023-08-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	1385710-1
ISSN	1435-8115 ; 1431-9276
ISSN (online)	1435-8115
ISSN	1431-9276
DOI	10.1093/micmic/ozad067.554
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Article ; Online: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model.

Rykiel, Graham / López, Claudia S / Riesterer, Jessica L / Fries, Ian / Deosthali, Sanika / Courchaine, Katherine / Maloyan, Alina / Thornburg, Kent / Rugonyi, Sandra

eLife

2020 Volume 9

Abstract: Cardiac pumping depends on the morphological structure of the heart, but also on its subcellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase ... ...

Abstract	Cardiac pumping depends on the morphological structure of the heart, but also on its subcellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three-dimensional (3D) heart structure, to assess malformations; and its ultrastructure, to assess organelle disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). In a small animal model (chicken embryo), we achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation on the same sample, enabling correlative multiscale imaging. Our approach enables multiscale studies in models of congenital heart disease and beyond.
MeSH term(s)	Animals ; Chick Embryo/cytology ; Chick Embryo/diagnostic imaging ; Chick Embryo/ultrastructure ; Heart/diagnostic imaging ; Heart/embryology ; Imaging, Three-Dimensional/methods ; Microscopy, Electron, Scanning/methods ; Myocardium/cytology ; Myocardium/ultrastructure ; X-Ray Microtomography/methods
Language	English
Publishing date	2020-10-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.58138
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Article: A workflow for visualizing human cancer biopsies using large-format electron microscopy.

Riesterer, Jessica L / López, Claudia S / Stempinski, Erin S / Williams, Melissa / Loftis, Kevin / Stoltz, Kevin / Thibault, Guillaume / Lanicault, Christian / Williams, Todd / Gray, Joe W

Methods in cell biology

2020 Volume 158, Page(s) 163–181

Abstract: Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular ... ...

Abstract	Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.
MeSH term(s)	Biopsy ; Data Analysis ; Humans ; Imaging, Three-Dimensional ; Microscopy, Electron, Scanning/methods ; Neoplasms/pathology ; Neoplasms/ultrastructure
Language	English
Publishing date	2020-03-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	0091-679X
ISSN	0091-679X
DOI	10.1016/bs.mcb.2020.01.005
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Article: A fully integrated, three-dimensional fluorescence to electron microscopy correlative workflow.

López, Claudia S / Bouchet-Marquis, Cedric / Arthur, Christopher P / Riesterer, Jessica L / Heiss, Gregor / Thibault, Guillaume / Pullan, Lee / Kwon, Sunjong / Gray, Joe W

Methods in cell biology

2017 Volume 140, Page(s) 149–164

Abstract: While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light ...

Abstract	While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light and electron microscopy (CLEM), including the fully integrated CLEM workflow instrument, the FEI CorrSight with MAPS, have allowed for a more reliable, reproducible, and quicker approach to correlate three-dimensional time-lapse confocal fluorescence data, with three-dimensional focused ion beam-scanning electron microscopy data. Here we demonstrate the entire integrated CLEM workflow using fluorescently tagged MCF7 breast cancer cells.
Language	English
Publishing date	2017
Publishing country	United States
Document type	Journal Article
ISSN	0091-679X
ISSN	0091-679X
DOI	10.1016/bs.mcb.2017.03.008
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Article ; Online: National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions.

Journal of proteome research

2020 Volume 19, Issue 5, Page(s) 1900–1912

Abstract: A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new ... ...

Abstract	A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of
MeSH term(s)	Biomarkers ; Biomarkers, Tumor/genetics ; Cancer Vaccines ; Immunotherapy ; National Cancer Institute (U.S.) ; Neoplasms ; Proteomics ; United States
Chemical Substances	Biomarkers ; Biomarkers, Tumor ; Cancer Vaccines
Language	English
Publishing date	2020-04-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.0c00021
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