Multi-template matching: a versatile tool for object-localization in microscopy images

Background The 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. Results We 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 ). Conclusion The 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.

[1]  Thorsten Meinl,et al.  KNIME - the Konstanz information miner: version 2.0 and beyond , 2009, SKDD.

[2]  Ravindra Peravali,et al.  Automated feature detection and imaging for high-resolution screening of zebrafish embryos. , 2011, BioTechniques.

[3]  Michael Unser,et al.  Principled Design and Implementation of Steerable Detectors , 2018, IEEE Transactions on Image Processing.

[4]  Markus Reischl,et al.  Automated high-throughput mapping of promoter-enhancer interactions in zebrafish embryos , 2009, Nature Methods.

[5]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Michael Tsang,et al.  Automated image‐based phenotypic analysis in zebrafish embryos , 2009, Developmental dynamics : an official publication of the American Association of Anatomists.

[8]  Gonzalo Navarro,et al.  Rotation and lighting invariant template matching , 2004, Inf. Comput..

[9]  Gloria Bueno,et al.  Glomerulus Classification and Detection Based on Convolutional Neural Networks , 2018, J. Imaging.

[10]  Tim Head,et al.  Binder 2.0 - Reproducible, interactive, sharable environments for science at scale , 2018, SciPy.

[11]  U. Liebel,et al.  Generation of orientation tools for automated zebrafish screening assays using desktop 3D printing , 2014, BMC Biotechnology.

[12]  César Domínguez,et al.  IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine , 2017, Comput. Biol. Medicine.

[13]  Roberto Brunelli,et al.  Template Matching Techniques in Computer Vision: Theory and Practice , 2009 .

[14]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[15]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[16]  Touradj Ebrahimi,et al.  Efficient Rotation-Discriminative Template Matching , 2007, CIARP.

[17]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[18]  E. Krupp,et al.  Automated Morphological Feature Assessment for Zebrafish Embryo Developmental Toxicity Screens , 2018, Toxicological sciences : an official journal of the Society of Toxicology.

[19]  Burkhard Tönshoff,et al.  Development of an Automated Imaging Pipeline for the Analysis of the Zebrafish Larval Kidney , 2013, PloS one.

[20]  Johannes Stegmaier,et al.  Automated high-throughput heart rate measurement in medaka and zebrafish embryos under physiological conditions , 2019, bioRxiv.

[21]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[22]  Hae Yong Kim,et al.  Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast , 2007, PSIVT.

[23]  Christian Eggeling,et al.  Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis , 2019 .

[24]  Christian Pylatiuk,et al.  High-Throughput Screening of Zebrafish Embryos Using Automated Heart Detection and Imaging , 2012, Journal of laboratory automation.

[25]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Manuel Théry,et al.  A new micropatterning method of soft substrates reveals that different tumorigenic signals can promote or reduce cell contraction levels. , 2011, Lab on a chip.

[27]  Min-Seok Choi,et al.  A novel two stage template matching method for rotation and illumination invariance , 2002, Pattern Recognit..

[28]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[29]  Ralf Mikut,et al.  An automated and high-throughput Photomotor Response platform for chemical screens , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[30]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Roberto Brunelli,et al.  Advanced , 1980 .

[32]  Gunjan Pandey,et al.  A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model , 2019, International journal of molecular sciences.