Multi-Template Matching: a versatile tool for object-localization in microscopy images

Thomas, Laurent S. V.; Gehrig, Jochen

doi:10.1101/619338

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…For example, there are standardized interfaces for data and parameters, flexible service-based architecture, as well as individually addressable parameters. We adapted our approach of curating the integration via ImageJ2 by incorporating the Multi-Template-Matching plugin 13 . An automated integration of all compatible operations was achieved by interfacing with the ImageJ Ops API 2 and applying on-demand conversion between JIPipe and Image2 data.…”

Section: Discussionmentioning

confidence: 99%

“…Operations on these data, available as menu commands in ImageJ are encapsulated into nodes with standardized interfaces for data transfer and parameters. This comprises common image processing utilities, including thresholding, extraction of measurements and ROI, as well as functions provided by popular plugins, including MorphoLibJ 11 , FeatureJ 12 , Multi-Template-Matching 13 , OMERO 14 , and Bio-Formats 15 . We also added support for CLIJ 6 to allow processing of images on graphics cards with the benefit of significantly increasing the performance.…”

Section: Symbiosis Of Imagej Andmentioning

confidence: 99%

“…JIPipe fills a niche by bridging the gap between researchers 10 with limited programming experience and the advancing development of modern methods of automated image processing. In particular, popular ImageJ plugins including MorphoLibJ 11 , FeatureJ 12 , CLIJ 6 , Multi-Template-Matching 13 , OMERO 14 , and Bio-Formats 15 can be accessed via JIPipe, as well as Cellpose 16 to perform deep learning by visual programming. Furthermore, JIPipe comes with nodes for Python 17 , Jython 18 , R 19 , and ImageJ macro scripts, thus extending the flexibility of workflow development for experienced users with programming skills.…”

Section: Introductionmentioning

confidence: 99%

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JIPipe: visual batch processing for ImageJ

2023

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The continuous development of new microscopy techniques requires the parallel evolution of image analysis workflows. ImageJ provides a high level of accessibility to bioimage processing, which is still impeded by the necessity of developing scripts to achieve reproducibility, and to comply to the FAIR principles. We provide a visual language termed JIPipe that allows the construction of an ImageJ workflow purely by designing a flowchart. We already included over 1000 functions from ImageJ and its plugins. In return, ImageJ is extended with custom-designed algorithms, thus forming a symbiotic relationship with JIPipe. Our software includes a fully reproduceable and standardized project format, zero-cost scalability of pipelines, as well as automated data saving into an open format. JIPipe was already utilized to solve numerous demanding image analysis tasks, showcasing its wide applicability and adaptability. JIPipe contributes towards making bioimage analysis more accessible, thereby fostering collaborations between experimentalists and computer scientists.

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Section: Discussionmentioning

confidence: 99%

Section: Symbiosis Of Imagej Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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JIPipe: visual batch processing for ImageJ

2023

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“…In recent years, scholars have conducted extensive research on product defect detection using machine vision technology, especially for PCBs, chips, and other products. Tomas and Gehrig [1] designed a multifunctional tool for object detection and localization in microscopes based on the multitemplate matching method. Hu and Wang [2] proposed a deep learning-based image detection method for PCB defect detection based on faster R-CNN.…”

Section: Introductionmentioning

confidence: 99%

LED Chip Defect Detection Method Based on a Hybrid Algorithm

Zheng

Lou

Wan

et al. 2023

International Journal of Intelligent Systems

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LED is an extremely important energy-saving lighting products, which has greatly facilitated human life. Meanwhile, it also makes a positive contribution to global carbon neutrality and carbon peaking. Defect detection is a vital part of the production process to control the quality of LED chips. The traditional methods use a microscope for manual visual inspection, which is time-consuming and has inconsistent testing standards, low efficiency, and other deficiencies. To solve these problems, a hybrid algorithm based on geometric computation and a convolutional neural network is proposed for LED chip defect detection. The method takes advantage of the dimensionality reduction of geometric computation to perform coarse detection of defects on preprocessed chip lithography graphs in the form of grid segmentation, which realizes fast coarse screening of large-scale chip samples and reduces postcomputational costs. The convolutional neural network model is used for the secondary fine detection of “suspected defective” chips after geometric coarse screening, and the SPP (spatial pyramid pooling) network model is improved by directly introducing the original feature map into the SPP pooling layer for summation to enhance the global and local feature information of the output feature map. Furthermore, we construct an LED chip image acquisition platform using a high-frequency multimagnification zoom lens, collect training samples of defective chips, and increase the number of samples through image processing techniques. The research introduces the R-CNN, SDD, and YOLO methods to evaluate the superiority of our method in a number of experiments. The experimental results show that our algorithm proposed in this paper has an average precision (AP) of 96.7% for large-scale chip detection with a low defect rate. Compared with other methods, the testing mean average precision (mAP) is 10.39% higher than traditional YOLO v2. The testing mIoU is also 3.63% higher than traditional YOLO v5, the detection speed is also significantly improved, and it has good robustness.

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“…Automated pose and behavior annotation tools for model organisms are increasingly accessible. While traditional computational methods for zebrafish pose annotation include thresholding and skeletonization (20,21) or template-fitting (22,23), many newer crossspecies methods prioritize deep learning. Such tools include DeepLabCut (24), DeepPoseKit (25), OptiFlex (26), and SLEAP (27).…”

Section: Introductionmentioning

confidence: 99%

Functional Trajectories during innate spinal cord repair

Jensen

Burris

Zhou

et al. 2023

Preprint

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Adult zebrafish are capable of anatomical and functional recovery following severe spinal cord injury. Axon growth, glial bridging and adult neurogenesis are hallmarks of cellular regeneration during spinal cord repair. However, the correlation between these cellular regenerative processes and functional recovery remains to be elucidated. Whereas the majority of established functional regeneration metrics measure swim capacity, we hypothesize that gait quality is more directly related to neurological health. Here, we performed a longitudinal swim tracking study for sixty individual zebrafish spanning eight weeks of spinal cord regeneration. Multiple swim parameters as well as axonal and glial bridging were integrated. We established rostral compensation as a new gait quality metric that highly correlates with functional recovery. Tensor component analysis of longitudinal data supports a correspondence between functional recovery trajectories and neurological outcomes. Moreover, our studies predicted and validated that a subset of functional regeneration parameters measured 1 to 2 weeks post-injury is sufficient to predict the regenerative outcomes of individual animals at 8 weeks post-injury. Our findings established new functional regeneration parameters and generated a comprehensive correlative database between various functional and cellular regeneration outputs.

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Multi-Template Matching: a versatile tool for object-localization in microscopy images

Abstract: 21We implemented multiple template matching as both a Fiji plugin and a KNIME workflow, providing 22an easy-to-use method for the automatic localization of objects of interest in images. We demonstrate 23

Cited by 4 publications

References 13 publications

JIPipe: visual batch processing for ImageJ

JIPipe: visual batch processing for ImageJ

LED Chip Defect Detection Method Based on a Hybrid Algorithm

Functional Trajectories during innate spinal cord repair

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