{"entity": "journal", "iuid": "a36010ba6ff1438aacda699753c9aa58", "timestamp": "2026-07-20T23:06:53.392Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Cytometry%20A.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Cytometry%20A"}}, "title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "publications_count": 6, "publications": [{"entity": "publication", "iuid": "f2fe68a2bd124841ae2931b1c3413fda", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f2fe68a2bd124841ae2931b1c3413fda.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f2fe68a2bd124841ae2931b1c3413fda"}}, "title": "PACMan: A software package for automated single-cell chlorophyll fluorometry.", "authors": [{"family": "Pont\u00e9n", "given": "Olle", "initials": "O", "orcid": "0000-0002-8061-2060", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/137743ee1acd4c90a6ce1f620a5f5949.json"}}, {"family": "Xiao", "given": "Linhong", "initials": "L"}, {"family": "Kutter", "given": "Jeanne", "initials": "J"}, {"family": "Cui", "given": "Yuan", "initials": "Y"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}, {"family": "Behrendt", "given": "Lars", "initials": "L", "orcid": "0000-0002-8988-2032", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/037bb8f29ff24e95b3358ab1f0ff2ee6.json"}}], "type": "journal article", "published": "2024-03-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "volume": "105", "issue": "3", "pages": "203-213", "issn-l": "1552-4922"}, "abstract": "Microalgae, small photosynthetic unicells, are of great interest to ecology, ecotoxicology and biotechnology and there is a growing need to investigate the ability of cells to photosynthesize under variable conditions. Current strategies involve hand-operated pulse-amplitude-modulated (PAM) chlorophyll fluorimeters, which can provide detailed insights into the photophysiology of entire populations- or individual cells of microalgae but are typically limited in their throughput. To increase the throughput of a commercially available MICROSCOPY-PAM system, we present the PAM Automation Control Manager ('PACMan'), an open-source Python software package that automates image acquisition, microscopy stage control and the triggering of external hardware components. PACMan comes with a user-friendly graphical user interface and is released together with a stand-alone tool (PAMalysis) for the automated calculation of per-cell maximum quantum efficiencies (= Fv /Fm ). Using these two software packages, we successfully tracked the photophysiology of >1000 individual cells of green algae (Chlamydomonas reinhardtii) and dinoflagellates (genus Symbiodiniaceae) within custom-made microfluidic devices. Compared to the manual operation of MICROSCOPY-PAM systems, this represents a 10-fold increase in throughput. During experiments, PACMan coordinated the movement of the microscope stage and triggered the MICROSCOPY-PAM system to repeatedly capture high-quality image data across multiple positions. Finally, we analyzed single-cell Fv /Fm with the manufacturer-supplied software and PAMalysis, demonstrating a median difference <0.5% between both methods. We foresee that PACMan, and its auxiliary software package will help increase the experimental throughput in a range of microalgae studies currently relying on hand-operated MICROSCOPY-PAM technologies.", "doi": "10.1002/cyto.a.24808", "pmid": "37864330", "labels": {"Lars Behrendt": null, "SciLifeLab Fellow": null}, "xrefs": [], "notes": [], "created": "2023-11-29T19:53:59.960Z", "modified": "2025-11-27T13:32:00.096Z"}, {"entity": "publication", "iuid": "3fab25f2fb434804aa7fd393e0d5e104", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/3fab25f2fb434804aa7fd393e0d5e104.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/3fab25f2fb434804aa7fd393e0d5e104"}}, "title": "Deep Learning in Image Cytometry: A Review.", "authors": [{"family": "Gupta", "given": "Anindya", "initials": "A", "orcid": "0000-0003-3557-4947", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/57cb44fb9e0a42ba8f91e813e2e45c76.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ", "orcid": "0000-0003-4046-9017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ef739cc45eff40b98c80ecc77f3afb73.json"}}, {"family": "Wieslander", "given": "H\u00e5kan", "initials": "H", "orcid": "0000-0002-6289-7285", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/72067b4a36de4d68a2200110df7cef4b.json"}}, {"family": "Pielawski", "given": "Nicolas", "initials": "N", "orcid": "0000-0001-8182-0091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a001a04d47014ba4af7cccb48b669783.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d2d38e59d5c840a0a53a4b90690fdc7a.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5c33a5b8cef4cad8b9f57b4510fb0c2.json"}}, {"family": "Suveer", "given": "Amit", "initials": "A", "orcid": "0000-0002-7779-094X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/caf81c0056e8483c9f072e06453ab67d.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bda9a501396248b5a7daa41db01518dc.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "I", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8dbd58070a9b4985b8bff09c1f965413.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}], "type": "journal article", "published": "2019-04-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "volume": "95", "issue": "4", "pages": "366-380"}, "abstract": "Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. \u00a9 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23701", "pmid": "30565841", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6590257"}], "notes": [], "created": "2024-11-05T16:04:36.061Z", "modified": "2024-11-29T09:31:39.875Z"}, {"entity": "publication", "iuid": "ce4d40b422c34742882b2f360c033b5e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ce4d40b422c34742882b2f360c033b5e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ce4d40b422c34742882b2f360c033b5e"}}, "title": "Objective automated quantification of fluorescence signal in histological sections of rat lens.", "authors": [{"family": "Talebizadeh", "given": "Nooshin", "initials": "N"}, {"family": "Hagstr\u00f6m", "given": "Nanna Zhou", "initials": "NZ"}, {"family": "Yu", "given": "Zhaohua", "initials": "Z"}, {"family": "Kronschl\u00e4ger", "given": "Martin", "initials": "M"}, {"family": "S\u00f6derberg", "given": "Per", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C"}], "type": "journal article", "published": "2017-08-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "volume": "91", "issue": "8", "pages": "815-821", "issn-l": "1552-4922"}, "abstract": "Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab\u2122. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n\u2009=\u2009395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. \u00a9 2017 International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23131", "pmid": "28494118", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T12:56:52.123Z", "modified": "2018-12-05T12:56:52.141Z"}, {"entity": "publication", "iuid": "8807f96189f64482a07e8a097e39e1cf", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8807f96189f64482a07e8a097e39e1cf.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8807f96189f64482a07e8a097e39e1cf"}}, "title": "Metastasis detection from whole slide images using local features and random forests.", "authors": [{"family": "Valkonen", "given": "Mira", "initials": "M"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K"}, {"family": "Liimatainen", "given": "Kaisa", "initials": "K"}, {"family": "Nykter", "given": "Matti", "initials": "M"}, {"family": "Latonen", "given": "Leena", "initials": "L"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P"}], "type": "journal article", "published": "2017-06-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "volume": "91", "issue": "6", "pages": "555-565"}, "abstract": "Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. \u00a9 2017 International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23089", "pmid": "28426134", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [], "notes": [], "created": "2024-11-05T16:04:31.056Z", "modified": "2024-11-29T12:05:56.034Z"}, {"entity": "publication", "iuid": "5e7479afc7d542c7b113fa70e8ed6335", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5e7479afc7d542c7b113fa70e8ed6335.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5e7479afc7d542c7b113fa70e8ed6335"}}, "title": "Global gray-level thresholding based on object size.", "authors": [{"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C"}], "type": "journal article", "published": "2016-04-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "volume": "89", "issue": "4", "pages": "385-390", "issn-l": "1552-4922"}, "abstract": "In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes. \u00a9 2016 International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.22806", "pmid": "26800009", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T12:35:44.069Z", "modified": "2018-12-05T12:35:44.088Z"}, {"entity": "publication", "iuid": "0bcb642890dc45dba6355ca786e4cfd5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0bcb642890dc45dba6355ca786e4cfd5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0bcb642890dc45dba6355ca786e4cfd5"}}, "title": "Automated classification of multicolored rolling circle products in dual-channel wide-field fluorescence microscopy.", "authors": [{"family": "Gavrilovic", "given": "Milan", "initials": "M"}, {"family": "Weibrecht", "given": "Irene", "initials": "I"}, {"family": "Conze", "given": "Tim", "initials": "T"}, {"family": "S\u00f6derberg", "given": "Ola", "initials": "O"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C"}], "type": "journal article", "published": "2011-07-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "volume": "79", "issue": "7", "pages": "518-527", "issn-l": "1552-4922"}, "abstract": "Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.", "doi": "10.1002/cyto.a.21087", "pmid": "21671402", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T12:06:43.964Z", "modified": "2018-12-05T12:06:43.998Z"}], "created": "2018-12-05T12:06:43.979Z", "modified": "2020-11-27T13:12:55.576Z"}