Image annotation

It is a tool to visualize and annotate volume image data of electron microscopy. Users can annotate objects (e.g. neurons) and skeleton structures. It provides the ability to overlaying the image data with user annotations, representing the spatial structure and the connectivity of labeled objects, and displaying a three dimensional model of it. It can be extended by plugins written in python. A similar, web-based implementation is being developed at webknossos.info. Example datasets are also available.

Cytomine is a rich internet application using modern web and distributed technologies (Grails, HTML/CSS/Javascript, Docker), databases (spatial SQL and NoSQL), and machine learning (tree-based approaches with random subwindows) to foster active and distributed collaboration and ease large-scale image exploitation.

It provides remote and collaborative principles, rely on data models that allow to easily organize and semantically annotate imaging datasets in a standardized way (using user-defined ontologies associated to regions of interest), efficiently support high-resolution multi-gigapixel images (incl. major digital scanner image formats), and provide mechanisms to readily proofread and share image quantifications produced by any image recognition algorithms.

By emphasizing collaborative principles, the aim of Cytomine is to accelerate scientific progress and to significantly promote image data accessibility and reusability. Cytomine allows to break common practices in this domain where imaging datasets, quantification results, and associated knowledge are still often stored and analyzed within the restricted circle of a specific laboratory.

This software is e.g. being used by life scientists in to help them better evaluate drug treatments or understand biological processes directly from whole-slide tissue images (digital histology), by pathologists to share and ease their diagnosis, and by teachers and students for pathology training purposes. It is also used in various microscopy applications.

Cytomine can be used as a stand-alone application (e.g. on a laptop) or on larger servers for collaborative works.

Cytomine implements object classification, image segmentation, content-based image retrieval, object counting, and interest point detection algorithms using machine learning.

ITK-SNAP is a software application used to segment structures in 3D medical images. It can also be used as a 3D annotation tool for deep learning. It is based on ITK, VTK libraries.

**Collaborative Annotation Toolkit for Massive Amounts of Image Data** CATMAID is a Collaborative Annotation Toolkit for Massive Amounts of Image Data. It is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by GoogleMaps, with which it shares basic navigation concepts, enhanced to allow the exploration of 3D biological image data acquired by optical or physical sectioning microscopy techniques. The interface enables seamless sharing of regions of interest through bookmarks and synchronized navigation through multiple registered data sets. With massive biological image data sets it is unrealistic to create a sustainable centralized repository. A unique feature of CATMAID is its partially decentralized architecture where the presented image data can reside on any Internet accessible server and yet can be easily cross-referenced in the central database. In this way no image data are duplicated and the data producers retain full control over their images. CATMAID is intended to serve as data sharing platform for biologists using high-resolution imaging techniques to probe large specimens. Any high-throughput, high-content imaging project such as gene expression pattern screens would benefit from the interface for data sharing and annotation.

ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif