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Hi,

I am getting backend error when using full text search, for example: https://biii.eu/search?search_api_fulltext=imagej

error:
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Backend fetch failed

Guru Meditation:

XID: 631819717

Varnish cache server
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Proxywave is software designed to assist users in creating and managing their own mobile proxies using wifi dongles. With Proxywave, users can easily create mobile proxy without any technical knowledge or experience. This platform provides a cost-effective solution for users who want to create their own mobile proxies and start their own proxy businesses. With the ability to create your own mobile proxy, Proxywave offers users a simple and easy-to-assemble solution for their proxy needs.

Image segmentation and object detection performance measures

The goal of this package is to provide easy-to-use tools for evaluation of the performance of segmentation methods in biomedical image analysis and beyond, and to fasciliate the comparison of different methods by providing standardized implementations. This package currently only supports 2-D image data.

SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.

MATLAB app to characterize nanoparticles imaged with super-resolution microscopy. nanoFeatures will read text and csv files from the NIKON and ONI microscopes and from the ThunderSTORM Fiji plugin, then cluster the localizations and filter by size and sphericity and finally output nanoparticle features like size, aspect ratio, and number of localizations per cluster (total and for each channel).

These are commands that create or process binary (black and white) images. Typical morphological operations/functions can be found here.

Check out this image.sc forum thread with many useful links about image analysis, recommended by experts! The list is live and growing! We (continuously) summarize this list here as well. 

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.
It implements a client-server architecture. The server is built as a web application that serves deep learning-based algorithms. The client application is implemented by extending Mastodon, providing a user interface for annotation, proofreading and visualization.

ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy

ZeroCostDL4Mic is a collection of self-explanatory Jupyter Notebooks for Google Colab that features an easy-to-use graphical user interface. They are meant to quickly get you started on learning to use deep-learning for microscopy. 

BiaPy is an open source Python library for building bioimage analysis pipelines. 

btrack is a Python library for multi object tracking, used to reconstruct trajectories in crowded fields. btrack implemented a residual U-Net model coupledd with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, btrack developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data.

Open source deep learning based framework for multi-animal pose tracking. It can track animal and any number of animals and has a labeling/training GUI for learning and proofreading.

Algorithm and software created to extract animal trajectories from videos of a collection of animals up to 100 individuals. Idtrackerai uses two convolutional networks: one for animal identification and another to detect when animals touch or cross each other.

The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel-level re- annotation of the data.

This workflow describes a deep-learning based pipeline for reliable single-organoid segmentation and tracking in 2D+t high-resolution brightfield microscopy of mouse mammary epithelial organoids. The pipeline involves a four-layer U-Net to infer semantic segmentation predictions, adaptive morphological filtering to establish candidate organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking step to associate the corresponding organoid instances in time.

OrganoSeg is an open-source software that integrates segmentation, filtering, and analysis for breast-cancer spheroid and colon and colorectal-cancer organoid morphologies.

OrganoID is an image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids.

JIPipe is a visual programming language to realize code-free workflow building for ImageJ-based image analyses. GUI, graphical user interface. Currently, JIPipe unifies the functionality of over 1,000 ImageJ commands into a standardized interface, represented as nodes in the pipeline flow chart. The window-based data management implemented in ImageJ is replaced with a table-based model designed for batch processing. JIPipe is also available from within the ImageJ update service.

The 3D ImageJ Suite is a set of algorithms and tools (mostly ImageJ plugins) developed since 2010, originally for 3D analysis of fluorescence microscopy. Since then, the plugins have been widely used and cited more than 200 times in biological journals. In this presentation we will give a general introduction to the tools available in the 3D ImageJ Suite : filtering, 3D segmentation for spots and nuclei, and 3D analysis. A graphical interface to manage 3D objects, the 3DManager, was also developed and will be presented.

The NEUBIAS Academy at home about CLIJ2 gives an introduction to accelerated image processing using Graphics Processing Units (GPUs) in ImageJ/Fiji. Core concepts are explained as well as usage of the tools with the ImageJ Macro recorder and auto-completion in Fijis script editor. Furthermore, an outlook is provided of how the CLIJ project will develop in the coming years to provide long-term maintained access to GPU-acceleration in the Bio-Image Analysis context.