{"entity": "researcher", "timestamp": "2026-06-15T17:31:27.752Z", "family": "Sintorn", "given": "Ida\u2010Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "affiliations": ["Centre for Image AnalysisUppsala University Uppsala 75124 Sweden"], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b"}}, "publications": [{"entity": "publication", "iuid": "6f1b382028f74b88ac1be4c6c2cb017d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d"}}, "title": "SimSearch: A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images.", "authors": [{"family": "Gupta", "given": "Ankit", "initials": "A", "orcid": "0000-0002-9961-1041", "researcher": {"href": "https://publications.scilifelab.se/researcher/ef29c8c1354a4c6d9ca8b0edaa034710.json"}}, {"family": "Sabirsh", "given": "Alan", "initials": "A", "orcid": "0000-0001-5310-0281", "researcher": {"href": "https://publications.scilifelab.se/researcher/0000d5f644194d6f8daf8b7f5dd0540c.json"}}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "issn-l": null, "volume": "26", "issue": "8", "pages": "4079-4089"}, "abstract": "Large-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments.\n\nThe user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches.\n\nThe framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction.\n\nSimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction.\n\nSimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.", "doi": "10.1109/JBHI.2022.3177602", "pmid": "35609108", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2022-11-02T06:18:25.917Z", "modified": "2023-06-19T12:58:45.375Z"}, {"entity": "publication", "iuid": "fe38a9e085de4033b895eda33e1ecee9", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9"}}, "title": "Deep Learning in Image Cytometry: A Review.", "authors": [{"family": "Gupta", "given": "Anindya", "initials": "A", "orcid": "0000-0003-3557-4947", "researcher": {"href": "https://publications.scilifelab.se/researcher/f5ba336ef3ab4d819a2f367e0e4f988c.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ", "orcid": "0000-0003-4046-9017", "researcher": {"href": "https://publications.scilifelab.se/researcher/bbabe660124f4d96bcc4f604f61e569e.json"}}, {"family": "Wieslander", "given": "H\u00e5kan", "initials": "H", "orcid": "0000-0002-6289-7285", "researcher": {"href": "https://publications.scilifelab.se/researcher/a271478f5e2c407f9e5f9aceb6b92e10.json"}}, {"family": "Pielawski", "given": "Nicolas", "initials": "N", "orcid": "0000-0001-8182-0091", "researcher": {"href": "https://publications.scilifelab.se/researcher/a8bf24da074a451fa245ff20f07e4b48.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications.scilifelab.se/researcher/da3da754a0264d538a98d8a85874aec1.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications.scilifelab.se/researcher/24c62ca6579b4055a95d0dba53722939.json"}}, {"family": "Suveer", "given": "Amit", "initials": "A", "orcid": "0000-0002-7779-094X", "researcher": {"href": "https://publications.scilifelab.se/researcher/2079df9478364042935868f40b69b113.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications.scilifelab.se/researcher/605dbd52684d4e54ae4150a9933abe6e.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.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": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC6590257"}], "notes": [], "created": "2018-12-30T16:32:19.557Z", "modified": "2023-06-19T12:59:01.277Z"}, {"entity": "publication", "iuid": "18346d9935a9491282d59dfb6b4cd277", "links": {"self": {"href": "https://publications.scilifelab.se/publication/18346d9935a9491282d59dfb6b4cd277.json"}, "display": {"href": "https://publications.scilifelab.se/publication/18346d9935a9491282d59dfb6b4cd277"}}, "title": "A short feature vector for image matching: The Log-Polar Magnitude feature descriptor.", "authors": [{"family": "Matuszewski", "given": "Damian J", "initials": "DJ", "orcid": "0000-0002-6148-5174", "researcher": {"href": "https://publications.scilifelab.se/researcher/0329e24125c3443e99e51435bea6af85.json"}}, {"family": "Hast", "given": "Anders", "initials": "A"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2017-11-30", "journal": {"volume": "12", "issn": "1932-6203", "issue": "11", "pages": "e0188496", "title": "PLoS ONE", "issn-l": "1932-6203"}, "abstract": "The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor-a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.", "doi": "10.1371/journal.pone.0188496", "pmid": "29190737", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "PONE-D-17-08232"}, {"db": "pmc", "key": "PMC5708636"}], "notes": [], "created": "2018-10-28T08:15:53.022Z", "modified": "2023-06-19T12:59:58.028Z"}, {"entity": "publication", "iuid": "47523d44b099420897fadd4070f0a53f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/47523d44b099420897fadd4070f0a53f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/47523d44b099420897fadd4070f0a53f"}}, "title": "PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data.", "authors": [{"family": "Matuszewski", "given": "Damian J", "initials": "DJ", "orcid": "0000-0002-6148-5174", "researcher": {"href": "https://publications.scilifelab.se/researcher/0329e24125c3443e99e51435bea6af85.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Puigvert", "given": "Jordi Carreras", "initials": "JC"}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2016-03-17", "journal": {"volume": "11", "issn": "1932-6203", "issue": "3", "pages": "e0151554", "title": "PLoS ONE", "issn-l": "1932-6203"}, "abstract": "Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler-a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.", "doi": "10.1371/journal.pone.0151554", "pmid": "26987120", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC4795740"}, {"db": "pii", "key": "PONE-D-15-54719"}], "notes": [], "created": "2018-10-28T08:19:20.514Z", "modified": "2023-06-19T13:00:20.163Z"}]}