ArticleBMC bioinformatics2020
Multi-template matching: a versatile tool for object-localization in microscopy images.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- How endosomal PIKfyve inhibition prevents viral membrane fusion and entry.bioRxiv : the preprint server for biology · 2025Article
- DeepQR: single-molecule QR codes for optical gene-expression analysis.Nanophotonics (Berlin, Germany) · 2025Article
- Cell state-specific cytoplasmic density controls spindle architecture and scaling.Nature cell biology · 2025Article
- Dysregulation of N-terminal acetylation causes cardiac arrhythmia and cardiomyopathy.Nature communications · 2025Article
- Inhibitory circuit motifs in Drosophila larvae generate motor program diversity and variability.PLoS biology · 2025Article
- Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications.Sensors (Basel, Switzerland) · 2024Article
- Efficient and reproducible generation of human iPSC-derived cardiomyocytes and cardiac organoids in stirred suspension systems.Nature communications · 2024Article
- Efficient and reproducible generation of human iPSC-derived cardiomyocytes using a stirred bioreactor.bioRxiv : the preprint server for biology · 2024Article
- Identification of side effects of COVID-19 drug candidates on embryogenesis using an integrated zebrafish screening platform.Scientific reports · 2023Article
- Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking.Bioengineering & translational medicine · 2023Article
- Functional trajectories during innate spinal cord repair.Frontiers in molecular neuroscience · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
backgroundThe localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity.
resultsWe provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github.com/multi-template-matching).
conclusionThe novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.