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.
Highlights
The localization of objects of interest is a key initial step in most image analysis workflows
To overcome current limitations in object-recognition, we report a new implementation of template matching with enhanced detection capacity by performing the search with multiple template images, improving the range of detectable patterns
We illustrate the robust localization of zebrafish eye regions within previously detected head region using a custom 2-step template matching Fiji macro (Additional file 9: Figure S6B, C, Additional file 3)
Summary
The localization of objects of interest is a key initial step in most image analysis workflows. 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. Detection of objects in microscopy images relies on classic intensity-based segmentation techniques that perform well for the localization of fluorescent objects These approaches often require the creation of Thomas and Gehrig BMC Bioinformatics (2020) 21:44 detection capacities [6,7,8,9,10]; setting up the software environment can be overwhelming and training the machine requires large amounts of annotated data, rendering it often inaccessible to most microscopy users.
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