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Готові списки джерел за темами / Microscopy image annotation / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Microscopy image annotation.

Статті в журналах з теми "Microscopy image annotation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Rydell, Christopher, and Joakim Lindblad. "CytoBrowser: a browser-based collaborative annotation platform for whole slide images." F1000Research 10 (March 22, 2021): 226. http://dx.doi.org/10.12688/f1000research.51916.1.

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Анотація:

We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in

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2

Englbrecht, Fabian, Iris E. Ruider, and Andreas R. Bausch. "Automatic image annotation for fluorescent cell nuclei segmentation." PLOS ONE 16, no. 4 (2021): e0250093. http://dx.doi.org/10.1371/journal.pone.0250093.

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Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully aut

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3

Holroyd, Natalie Aroha, Claire Walsh, Lucie Gourmet, and Simon Walker-Samuel. "Quantitative Image Processing for Three-Dimensional Episcopic Images of Biological Structures: Current State and Future Directions." Biomedicines 11, no. 3 (2023): 909. http://dx.doi.org/10.3390/biomedicines11030909.

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Episcopic imaging using techniques such as High Resolution Episcopic Microscopy (HREM) and its variants, allows biological samples to be visualized in three dimensions over a large field of view. Quantitative analysis of episcopic image data is undertaken using a range of methods. In this systematic review, we look at trends in quantitative analysis of episcopic images and discuss avenues for further research. Papers published between 2011 and 2022 were analyzed for details about quantitative analysis approaches, methods of image annotation and choice of image processing software. It is shown

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4

Samokhin, Yu V., and O. G. Avrunin. "Integration of cloud services for storage and processing of cryomicroscopic images: practical experience using MINIO and CVAT." Radiotekhnika, no. 221 (June 19, 2025): 83–88. https://doi.org/10.30837/rt.2025.2.221.11.

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In modern biomedical science, the efficient processing of large volumes of visual data is critically important for analyzing cellular structures. This abstract describes practical experience integrating the MinIO and CVAT cloud services to automate the processes of storage, annotation, and analysis of cryo-microscopy images. The application of these tools enhances the accuracy of cell segmentation, ensures scalability, and improves the reproducibility of research. Cryo-microscopy is a powerful method for visualizing biological samples at the nanoscale. However, processing the resulting images

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5

Hou, Vincent D. H. "Automatic Page-Layout Scripts for Gatan Digital Micrograph®." Microscopy and Microanalysis 7, S2 (2001): 976–77. http://dx.doi.org/10.1017/s1431927600030956.

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The software DigitalMicrograph (DM) by Gatan, Inc., is a popular software platform for digital imaging in microscopy. in a service-oriented microscopy laboratory, a large number of images from many different samples are generated each day. It is critical that each printed image is properly labeled with sample identification and a description before printing. with DM, a script language is provided: from this, various analyses can be designed or customized and repetitive tasks can be automated. This paper presents the procedures and DM scripts needed to perform these tasks. Due to the major soft

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6

Iakovidis, D. K., T. Goudas, C. Smailis, and I. Maglogiannis. "Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/286856.

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Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifyin

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7

Aeffner, Famke, Hibret A. Adissu, Michael C. Boyle, et al. "Digital Microscopy, Image Analysis, and Virtual Slide Repository." ILAR Journal 59, no. 1 (2018): 66–79. http://dx.doi.org/10.1093/ilar/ily007.

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Abstract Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the d

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8

Eschweiler, Dennis, Malte Rethwisch, Mareike Jarchow, Simon Koppers, and Johannes Stegmaier. "3D fluorescence microscopy data synthesis for segmentation and benchmarking." PLOS ONE 16, no. 12 (2021): e0260509. http://dx.doi.org/10.1371/journal.pone.0260509.

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Анотація:

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotatio

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9

Hao, Ruqian, Lin Liu, Jing Zhang, et al. "A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning." Journal of Healthcare Engineering 2022 (February 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/1929371.

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Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and reliable solution for an automatic early diagnosis of vaginitis. However, deep neural networks require massive well-annotated data. Manual annotation of microscopic images is highly cost extensive because it not only is a time-consuming process but also needs highly trained people (doctors, pathologists, or technicians). Most existing active learnin

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10

Burfeid-Castellanos, Andrea M., Michael Kloster, Sára Beszteri, et al. "A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples." Water 14, no. 20 (2022): 3332. http://dx.doi.org/10.3390/w14203332.

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Diatom identification and counting by light microscopy of permanently embedded acid-cleaned silicate shells (frustules) is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on “digital virtual slides”, and compare it to the traditional, non-digitized light microscopy workflow on freshwater samples. We analysed three replicate slides taken from six benthic samples using two methods: (1) working directly on a light microscope (the “traditional” counting method), and (2) preparing “virtual digital slides” by high-resolution sli

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11

Park, Ho-min, Sanghyeon Park, Maria Krishna de Guzman, et al. "MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams." PLOS ONE 17, no. 6 (2022): e0269449. http://dx.doi.org/10.1371/journal.pone.0269449.

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Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models

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12

Mill, Leonid, David Wolff, Nele Gerrits, et al. "Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation." Small Methods 5 (May 3, 2021): 2100223. https://doi.org/10.1002/smtd.202100223.

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Анотація:

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manu

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13

Mihelic, Samuel A., William A. Sikora, Ahmed M. Hassan, Michael R. Williamson, Theresa A. Jones, and Andrew K. Dunn. "Segmentation-Less, Automated, Vascular Vectorization." PLOS Computational Biology 17, no. 10 (2021): e1009451. http://dx.doi.org/10.1371/journal.pcbi.1009451.

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Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (bina

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14

Tommaso, Rodani, Osmenaj Elda, Cazzaniga Alberto, Panighel Mirco, Africh Cristina, and Cozzini Stefano. "Towards the FAIRification of Scanning Tunneling Microscopy Images, Data Intelligence (2023) 5 (1): 27–42." Data Intelligence 5, no. 1 (2023): 27–42. https://doi.org/10.1162/dint_a_00164.

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Published version Abstract: In this paper, we describe the data management practices and services developed for making FAIR compliant a scientific archive of Scanning Tunneling Microscopy (STM) images. As a first step, we extracted the instrument metadata of each image of the dataset to create a structured database. We then enriched these metadata with information on the structure and composition of the surface by means of a pipeline that leverages human annotation, machine learning techniques, and instrument metadata filtering. To visually explore both images and metadata, as well as to impro

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15

Chin, Shuang Yee, Jian Dong, Khairunnisa Hasikin, et al. "Bacterial image analysis using multi-task deep learning approaches for clinical microscopy." PeerJ Computer Science 10 (August 8, 2024): e2180. http://dx.doi.org/10.7717/peerj-cs.2180.

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Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable

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16

Viana da Silva, Matheus, Natália de Carvalho Santos, Julie Ouellette, Baptiste Lacoste, and Cesar H. Comin. "A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts." PLOS One 20, no. 5 (2025): e0322048. https://doi.org/10.1371/journal.pone.0322048.

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Анотація:

Creating a dataset for training supervised machine learning algorithms can be a demanding task. This is especially true for blood vessel segmentation since one or more specialists are usually required for image annotation, and creating ground truth labels for just a single image can take up to several hours. In addition, it is paramount that the annotated samples represent well the different conditions that might affect the imaged tissues as well as possible changes in the image acquisition process. This can only be achieved by considering samples that are typical in the dataset as well as aty

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17

Germani, Elodie, Hugues Lelouard, and Mathieu Fallet. "SAPHIR: a Shiny application to analyze tissue section images." F1000Research 9 (April 8, 2021): 1276. http://dx.doi.org/10.12688/f1000research.27062.2.

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Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurements when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation.

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18

Roncaglia, Paola, Dam Teunis J. P. van, Karen R. Christie, et al. "The Gene Ontology of eukaryotic cilia and flagella." Cilia 6, no. 1 (2017): 10. https://doi.org/10.1186/s13630-017-0054-8.

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<strong>Background: </strong>Recent research into ciliary structure and function provides important insights into inherited diseases termed ciliopathies and other cilia-related disorders. This wealth of knowledge needs to be translated into a computational representation to be fully exploitable by the research community. To this end, members of the Gene Ontology (GO) and SYSCILIA Consortia have worked together to improve representation of ciliary substructures and processes in GO.<strong>Methods: </strong>Members of the SYSCILIA and Gene Ontology Consortia suggested additions and changes to GO

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19

Germani, Elodie, Hugues Lelouard, and Mathieu Fallet. "SAPHIR: a Shiny application to analyze tissue section images." F1000Research 9 (October 27, 2020): 1276. http://dx.doi.org/10.12688/f1000research.27062.1.

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Анотація:

Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurement when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation.

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20

Zachariou, Marios, Ognjen Arandjelović, Wilber Sabiiti, Bariki Mtafya, and Derek Sloan. "Tuberculosis Bacteria Detection and Counting in Fluorescence Microscopy Images Using a Multi-Stage Deep Learning Pipeline." Information 13, no. 2 (2022): 96. http://dx.doi.org/10.3390/info13020096.

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The manual observation of sputum smears by fluorescence microscopy for the diagnosis and treatment monitoring of patients with tuberculosis (TB) is a laborious and subjective task. In this work, we introduce an automatic pipeline which employs a novel deep learning-based approach to rapidly detect Mycobacterium tuberculosis (Mtb) organisms in sputum samples and thus quantify the burden of the disease. Fluorescence microscopy images are used as input in a series of networks, which ultimately produces a final count of present bacteria more quickly and consistently than manual analysis by healthc

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21

Hay, Johnny, Eilidh Troup, Ivan Clark, Julian Pietsch, Tomasz Zieliński, and Andrew Millar. "PyOmeroUpload: A Python toolkit for uploading images and metadata to OMERO." Wellcome Open Research 5 (May 18, 2020): 96. http://dx.doi.org/10.12688/wellcomeopenres.15853.1.

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Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in

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22

Hay, Johnny, Eilidh Troup, Ivan Clark, Julian Pietsch, Tomasz Zieliński, and Andrew Millar. "PyOmeroUpload: A Python toolkit for uploading images and metadata to OMERO." Wellcome Open Research 5 (August 26, 2020): 96. http://dx.doi.org/10.12688/wellcomeopenres.15853.2.

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Анотація:

Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in

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23

Zováthi, Bendegúz H., Réka Mohácsi, Attila Marcell Szász, and György Cserey. "Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network." Diagnostics 12, no. 9 (2022): 2161. http://dx.doi.org/10.3390/diagnostics12092161.

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In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm cou

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24

Scherr, Tim, Katharina Löffler, Moritz Böhland, and Ralf Mikut. "Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy." PLOS ONE 15, no. 12 (2020): e0243219. http://dx.doi.org/10.1371/journal.pone.0243219.

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The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furtherm

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25

Bauer, Andreas, Magdalena Prechová, Lena Fischer, Ingo Thievessen, Martin Gregor, and Ben Fabry. "pyTFM: A tool for traction force and monolayer stress microscopy." PLOS Computational Biology 17, no. 6 (2021): e1008364. http://dx.doi.org/10.1371/journal.pcbi.1008364.

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Cellular force generation and force transmission are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy comput

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26

Wüstefeld, Konstantin, Robin Ebbinghaus, and Frank Weichert. "Learning to Segment Blob-like Objects by Image-Level Counting." Applied Sciences 13, no. 22 (2023): 12219. http://dx.doi.org/10.3390/app132212219.

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There is a high demand for manually annotated data in many of the segmentation tasks based on neural networks. Selecting objects pixel by pixel not only takes much time, but it can also lead to inattentiveness and to inconsistencies due to changing annotators for different datasets and monotonous work. This is especially, but not exclusively, the case with sensor data such as microscopy imaging, where many blob-like objects need to be annotated. In addressing these problems, we present a weakly supervised training method that uses object counts at the image level to learn a segmentation implic

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27

Doran, Simon J., Mohammad Al Sa’d, James A. Petts, et al. "Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies." Tomography 8, no. 1 (2022): 497–512. http://dx.doi.org/10.3390/tomography8010040.

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Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the chal

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28

Patino, Cesar A., Prithvijit Mukherjee, Vincent Lemaitre, Nibir Pathak, and Horacio D. Espinosa. "Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform." SLAS TECHNOLOGY: Translating Life Sciences Innovation 26, no. 1 (2021): 26–36. http://dx.doi.org/10.1177/2472630320982320.

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Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capable of automatically detecting cells with artificial intelligence (AI) software and delivering exogenous cargoes of different sizes with uniform dosage. We implemented a fully convolutional network (FCN) architecture to precisely locate the nuclei and cytosol of six cell types with various shapes and sizes, using phase contrast microscopy. Nuclear st

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29

Arnavaz, Kasra, Oswin Krause, Kilian Zepf, et al. "Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy." Machine Learning for Biomedical Imaging 1, June 2022 (2022): 1–25. http://dx.doi.org/10.59275/j.melba.2022-4bf2.

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Motivated by the challenging segmentation task of pancreatic tubular networks, this paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and expensive or difficult annotation. Our contributions are the following: a) We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth segmentations, applied to model selection and validation. b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data. In our method, we first use a s

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30

Parchment, Ralph E., Katherine Ferry-Galow, Hala R. Makhlouf, et al. "Suitability factors of core needle biopsies for pharmacodynamic (PD) studies." Journal of Clinical Oncology 35, no. 15_suppl (2017): 2540. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.2540.

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2540 Background: There are different requirements of biopsies for diagnosis vs. pharmacologic evaluation of drug mechanism biomarkers. Evaluation of core needle biopsy pairs collected pre-dose and at a defined timepoint post-dose provides insight into the pharmacodynamics of agents in early development. Adequate biopsies are key for quantifying response of the tumor cell population to molecular drug action. Tumor heterogeneity and variable tumor content make many biopsy pairs unsuitable for biomarker evaluation with any assay platform (microscopy, immunoassay, etc.). We analyzed biopsies obtai

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31

Schmidt, Christian, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, and Stefanie Weidtkamp-Peters. "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey." F1000Research 11 (June 10, 2022): 638. http://dx.doi.org/10.12688/f1000research.121714.1.

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Background Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods An online survey was conducted with a mixed question-type design. We created a questionnaire tailor

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Schmidt, Christian, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, and Stefanie Weidtkamp-Peters. "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey." F1000Research 11 (September 20, 2022): 638. http://dx.doi.org/10.12688/f1000research.121714.2.

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Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tail

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Williams, Bryan M., Davide Borroni, Rongjun Liu, et al. "An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study." Diabetologia 63, no. 2 (2019): 419–30. http://dx.doi.org/10.1007/s00125-019-05023-4.

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Abstract Aims/hypothesis Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, AC

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Liu, Ye, Sophia J. Wagner, and Tingying Peng. "Multi-Modality Microscopy Image Style Augmentation for Nuclei Segmentation." Journal of Imaging 8, no. 3 (2022): 71. http://dx.doi.org/10.3390/jimaging8030071.

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Annotating microscopy images for nuclei segmentation by medical experts is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it can not only deal with different cell assay types and lighting conditions, but also with different imaging modalities, such as bright-field and fluorescence microscopy. Using disentangled representations for content and style, we can preserve the structure of the original

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35

Tahir, Waleed, Yibo Zhang, Jun Zhang, Jacqueline Brosnan-Cashman, Robert Egger, and Justin Lee. "Abstract B008: Machine-learning enabled quantification of colocalized multiplex IHC signals with spectral overlap." Molecular Cancer Therapeutics 22, no. 12_Supplement (2023): B008. http://dx.doi.org/10.1158/1535-7163.targ-23-b008.

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Abstract Background: Immunohistochemistry (IHC) is the gold standard for detecting protein biomarkers in cancer. In breast cancer, several nuclear biomarkers are relevant for guiding treatment decisions, including estrogen receptor (ER) and progesterone receptor (PR), indicative of response to endocrine therapy, and the proliferation marker Ki67, indicative of response to CDK inhibition1. However, these nuclear markers are detected using chromogens that colocalize and spectrally overlap with hematoxylin (HTX), the standard nuclear counterstain for IHC. Thus, the accurate detection and quantifi

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Ng, Robert, Lakshmanan Govindasamy, Brittney L. Gurda, et al. "Structural Characterization of the Dual Glycan Binding Adeno-Associated Virus Serotype 6." Journal of Virology 84, no. 24 (2010): 12945–57. http://dx.doi.org/10.1128/jvi.01235-10.

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ABSTRACT The three-dimensional structure of adeno-associated virus (AAV) serotype 6 (AAV6) was determined using cryo-electron microscopy and image reconstruction and using X-ray crystallography to 9.7- and 3.0-Å resolution, respectively. The AAV6 capsid contains a highly conserved, eight-stranded (βB to βI) β-barrel core and large loop regions between the strands which form the capsid surface, as observed in other AAV structures. The loops show conformational variation compared to other AAVs, consistent with previous reports that amino acids in these loop regions are involved in differentiati

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Hanna, Matthew G., Ishtiaque Ahmed, Jeffrey Nine, Shyam Prajapati, and Liron Pantanowitz. "Augmented Reality Technology Using Microsoft HoloLens in Anatomic Pathology." Archives of Pathology & Laboratory Medicine 142, no. 5 (2018): 638–44. http://dx.doi.org/10.5858/arpa.2017-0189-oa.

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Context Augmented reality (AR) devices such as the Microsoft HoloLens have not been well used in the medical field. Objective To test the HoloLens for clinical and nonclinical applications in pathology. Design A Microsoft HoloLens was tested for virtual annotation during autopsy, viewing 3D gross and microscopic pathology specimens, navigating whole slide images, telepathology, as well as real-time pathology-radiology correlation. Results Pathology residents performing an autopsy wearing the HoloLens were remotely instructed with real-time diagrams, annotations, and voice instruction. 3D-scann

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Zeder, Michael, Silke Van den Wyngaert, Oliver KÃ¯Â¿Â½ster, Kathrin M. Felder, and Jakob Pernthaler. "Automated Quantification and Sizing of Unbranched Filamentous Cyanobacteria by Model-Based Object-Oriented Image Analysis." Applied and Environmental Microbiology 76, no. 5 (2010): 1615–22. http://dx.doi.org/10.1128/aem.02232-09.

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ABSTRACT Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-oriented image analysis to simultaneously determine (i) filament number, (ii) individual filam

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Noureddine, Kouther, Paul Gallagher, Martial Guillaud, and Calum MacAulay. "Abstract 1719: Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry & deep learning: A new approach to spatially map the tumor microenvironment." Cancer Research 82, no. 12_Supplement (2022): 1719. http://dx.doi.org/10.1158/1538-7445.am2022-1719.

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Abstract The tumor microenvironment (TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells & the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. At present, the usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more stained sections. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional

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Dweiri, Yazan, Mousa Al-Zanina, and Dominique Durand. "Enhanced Automatic Morphometry of Nerve Histological Sections Using Ensemble Learning." Electronics 11, no. 14 (2022): 2277. http://dx.doi.org/10.3390/electronics11142277.

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There is a need for an automated morphometry algorithm to facilitate the otherwise labor-intensive task of the quantitative histological analysis of neural microscopic images. A benchmark morphometry algorithm is the convolutional neural network Axondeepseg (ADS), which yields a high segmentation accuracy for scanning and transmission electron microscopy images. Nevertheless, it shows decreased accuracy when applied to optical microscopy images, and it has been observed to yield sizable false positives when identifying small-sized neurons within the slides. In this study, ensemble learning is

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Serbenyuk, A. V. "Uterine natural killer cells during the implantation window period in women veterans experienced by injury with unrealished reproductive function." Reports of Vinnytsia National Medical University 27, no. 1 (2023): 28–34. http://dx.doi.org/10.31393/reports-vnmedical-2023-27(1)-05.

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Annotation. Against the background of stress and post-traumatic stress disorder (PTSD) in women, changes in the hormonal background improve – the levels of stress hormones and the morphofunctional endometrium, which in their change negatively affect the reproductive health of women in Ukraine. The purpose of this study was to increase the efficiency of diagnosis and treatment of pathology and implantation capacity of the endometrium in women of reproductive age who took part in hostilities and suffered a concussion. Uterine natural killer cells were studied during the implantation window in 48

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Pécot, Thierry, Alexander Alekseyenko, and Kristin Wallace. "A deep learning segmentation strategy that minimizes the amount of manually annotated images." F1000Research 10 (March 30, 2021): 256. http://dx.doi.org/10.12688/f1000research.52026.1.

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Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditiona

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Pécot, Thierry, Alexander Alekseyenko, and Kristin Wallace. "A deep learning segmentation strategy that minimizes the amount of manually annotated images." F1000Research 10 (January 17, 2022): 256. http://dx.doi.org/10.12688/f1000research.52026.2.

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Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training dataset with data augmentation, the creation of an artificial dataset with a conditional

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Xing, Cheng, Ronald Xie, and Gary D. Bader. "RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset." PLOS Computational Biology 21, no. 5 (2025): e1013115. https://doi.org/10.1371/journal.pcbi.1013115.

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Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for analyzing EM images. While manual segmentation is reliable, it is labor-intensive, incentivizing the development of automated segmentation methods. Although deep learning-based segmentation has demonstrated expert-level performance, it lacks generalizable performance across diverse EM datasets. Current approaches usually use either convolutional or transformer-based neural networks for image feature extraction. We developed the RETINA method, which

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Baderot, J., M. Grould, D. Misra, et al. "Application of deep-learning based techniques for automatic metrology on scanning and transmission electron microscopy images." Journal of Vacuum Science & Technology B 40, no. 5 (2022): 054003. http://dx.doi.org/10.1116/6.0001988.

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Scanning or transmission electron microscopy (SEM/TEM) are standard techniques used during Research and Development (R&D) phases to study the structure and morphology of microscopic materials. Variety in object shapes and sizes are observed in such images to ensure robust micro- and nanomaterials critical dimension analysis. This way, precision and accuracy can be guaranteed during materials manufacturing processes. Such diversity and complexity in the data make it challenging to automatically extract the desired measurements of these microscopic structures. Existing tools in metrology oft

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Dorkenwald, Sven, Casey M. Schneider-Mizell, Derrick Brittain, et al. "CAVE: Connectome Annotation Versioning Engine." Nature Methods, April 9, 2025. https://doi.org/10.1038/s41592-024-02426-z.

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Abstract Advances in electron microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets, which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this changing and expanding data la

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Ali, Mohammed A. S., Kaspar Hollo, Tõnis Laasfeld, et al. "ArtSeg—Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-14703-y.

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AbstractBrightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by or

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Guérinot, Corentin, Valentin Marcon, Charlotte Godard, et al. "New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing." Frontiers in Bioinformatics 1 (January 31, 2022). http://dx.doi.org/10.3389/fbinf.2021.777101.

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Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to util

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Jerez, Diego, Eleanor Stuart, Kylie Schmitt, et al. "A deep learning approach to identifying immunogold particles in electron microscopy images." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-87015-2.

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AbstractElectron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold pa

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Borland, David, Carolyn M. McCormick, Niyanta K. Patel, et al. "Segmentor: a tool for manual refinement of 3D microscopy annotations." BMC Bioinformatics 22, no. 1 (2021). http://dx.doi.org/10.1186/s12859-021-04202-8.

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Abstract Background Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated

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