Relevant bibliographies by topics / Microscopy image annotation

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Academic literature on the topic 'Microscopy image annotation'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Microscopy image annotation"

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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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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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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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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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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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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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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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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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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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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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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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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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Book chapters on the topic "Microscopy image annotation"

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Del Rio, Mauro, Luca Lianas, Oskar Aspegren, et al. "AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_48.

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AbstractWhile studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide comput
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Tanaka, Yoji, Motoki Inaji, Daisu Abe, Kazuhide Shimizu, and Taketoshi Maehara. "Educational Impact of an Annotation System Integrated with an Exoscope for Cerebral Aneurysm Surgery: Case Description." In Acta Neurochirurgica Supplement. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89844-0_21.

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Abstract Purpose: The three-dimensional (3D) exoscope has been reported to have better image quality, ergonomics, and educational outcomes than a microscope. However, whether the exoscope can improve communication between the main surgeon and the mentor remains unclear. This chapter introduces our experience with using an exoscope and an annotation system for surgical education during a left middle cerebral aneurysm surgery in a 63-year-old woman. Methods: We used an annotation system integrated with a 3D exoscope during aneurysm surgery. The mentor provided instruction by using the annotation
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Neves, João C., Helena Castro, Hugo Proença, and Miguel Coimbra. "Automatic Annotation of Leishmania Infections in Fluorescence Microscopy Images." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_70.

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Khalid, Nabeel, Tiago Comassetto Froes, Maria Caroprese, et al. "PACE: Point Annotation-Based Cell Segmentation for Efficient Microscopic Image Analysis." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44210-0_44.

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Jirik, Miroslav, Vladimira Moulisova, Claudia Schindler, et al. "MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51002-2_15.

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Guo, Yuanhao, Yaoru Luo, Wenjing Li, and Ge Yang. "Fluorescence Microscopy Images Segmentation Based on Prototypical Networks with a Few Annotations." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_14.

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Khalid, Nabeel, Maria Caroprese, Gillian Lovell, et al. "Bounding Box Is All You Need: Learning to Segment Cells in 2D Microscopic Images via Box Annotations." In Medical Image Understanding and Analysis. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66955-2_22.

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Eschweiler, Dennis, Tim Klose, Florian Nicolas Müller-Fouarge, Marcin Kopaczka, and Johannes Stegmaier. "Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal Microscopy Images." In Simulation and Synthesis in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32778-1_9.

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Marzahl, Christian, Christof A. Bertram, Marc Aubreville, et al. "Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59710-8_3.

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Bozkurt, Alican, Kivanc Kose, Christi Alessi-Fox, et al. "A Multiresolution Convolutional Neural Network with Partial Label Training for Annotating Reflectance Confocal Microscopy Images of Skin." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_33.

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Conference papers on the topic "Microscopy image annotation"

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Liu, Chen, Danqi Liao, Alejandro Parada-Mayorga, Alejandro Ribeiro, Marcello DiStasio, and Smita Krishnaswamy. "DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888526.

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Mohammad, Rafiq Darwis, Deepak Devegowda, Chandra Rai, Mark Curtis, Sanjana Mudduluru, and Sai Kiran Maryada. "Self-Supervised Learning Using Vision Transformer Architecture for Rock Image Segmentation." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225609-ms.

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Abstract The segmentation of microstructural features in Scanning Electron Microscopy (SEM) images of shale samples is critical for petrophysical analyses, including mineralogy quantification, pore network analysis, and pore system identification. However, processing these images efficiently and accurately typically requires supervised deep learning-based methods, such as semantic segmentation algorithms. Semantic segmentation classifies each pixel in an image into a predefined category (e.g., organic material, inorganic material, pore), regardless of the number of times that category appears
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Serafin, Robert B., Rui Wang, Sarah Chow, et al. "Automatic detection of prostate cancer via 3D microscopy and deep learning." In Microscopy Histopathology and Analytics. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/microscopy.2024.mm3a.2.

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We present an annotation free deep-learning-assisted segmentation pipeline to automatically identify healthy and malignant glands in 3D microscopy images of prostate biopsies stained with fluorescent analogs of H&E.
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Sivaroopan, Nirhoshan, Hasindri Watawana, Chamuditha Jayanga, et al. "Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology." In Microscopy Histopathology and Analytics. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/microscopy.2024.mm3a.5.

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Neural networks can learn features from millions of histopathology images. However, curating high-quality annotations for training is laborious. In this work, we show how such models can be trained with only 1-10% of annotated data.
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Kose, Kivanc, Alican Bozkurt, Christi Alessi-Fox, et al. "A Multiresolution Deep Learning Framework for Automated Annotation of Reflectance Confocal Microscopy Images." In Microscopy Histopathology and Analytics. OSA, 2018. http://dx.doi.org/10.1364/microscopy.2018.mth2a.1.

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Matuszewski, Damian J., and Ida-Maria Sintorn. "Minimal annotation training for segmentation of microscopy images." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018. http://dx.doi.org/10.1109/isbi.2018.8363599.

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Falkenstein, Brian, Shannon Quinn, Chakra Chennubhotla, Filippo Pullara, and Raymond Yan. "Predx-Tools." In Python in Science Conference. SciPy, 2024. http://dx.doi.org/10.25080/ycfw5807.

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Histopathological images, which are digitized images of human or animal tissue, contain insights into disease state. Typically, a pathologist will look at a slide under a microscope to make decisions about prognosis and treatment. Due to the high complexity of the data, applying automatic image analysis is challenging. Often, human intervention in the form of manual annotation or quality control (QC) is required. Additionally, the data itself varies considerably in available features, size, and shape. Thus, a streamlined and interactive approach is a necessary part of any digital pathology pip
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Zhuge, Huimin, David Manthey, Kimberly Ashman, Brian Summa, Roni Choudhury, and J. Quincy Brown. "Interactive WSI Review and Annotation Tracker, and Digital Visualization Tool for Pathologist Diagnosis of Whole Slide Images." In Microscopy Histopathology and Analytics. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/microscopy.2022.mw3a.4.

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Chen, Yinda, Wei Huang, Shenglong Zhou, Qi Chen, and Zhiwei Xiong. "Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/68.

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The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron microscopy (EM) data. By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, man
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Zhang, Baosen, Xin Jin, Yitian Xiao, et al. "Quantitative Identification of Sandstone Lithology Based On Thin-Section Micrographs Using the U-net and U-net++ Semantic Segmentation Network." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22865-ea.

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Abstract Quantitative identification of sandstone microscopic images is an essential task for sandstone reservoir characterization. The widely-used classical Gazzi-Dickinson point-counting method can be subjective, inconsistent and time-consuming. Furthermore, by directly putting labeled microscopic images of all rock types into image recognition models for training, most previous studies did not address the petrographic principle of artificial identification. In this study, U-Net and U-Net++ semantic segmentation networks that incorporated the sandstone petrographic principle in quantitative
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Reports on the topic "Microscopy image annotation"

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Greaves, C., and J. B. R. Eamer. Focus stacking for cataloguing, presentation, and identification of microfossils in marine sediments. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331355.

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Microfossils represent an important part of studying past depositional environments and determining ages for the strata they are found within. The key to ascribing paleoenvironmental interpretations to the sediments in which a microfossil is found is accurate identification of the microfossil. A number of techniques can be used to identify microfossils, including ones that use key features, morphologies, and characteristics from imagery acquired using a scanning electron microscope. A low-cost, efficient alternative method is digital photography of optical microscope images. This technical not
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