Commercial

Columbus Image Data Storage and Analysis System

Collection
Description

Columbus is a combination of an image database (based on Omero, OME) and an image analysis engine based on Acapella (PerkinElmer). It is dedicated to cell culture based high content screening data and is used via a web interface. It provides a set importers for automated microscopes such as Yokogawa CellVoyager, PerkinElmer Operetta, PerkinElmer Opera and data in Metamorph format. After login, Images can be explored in a standard web browser by clicking on a well plate view. Image analysis workflows can be developed by combining modules like "find nuclei", "find cytoplasm", "find spots" for object detection. Objects can have a hierarchical structure, e.g. spot objects can be part of a cell object. The approach of workflow design is similar to the freeware cell profiler, but more restricted (less functions and less parameters to tweak) and easier to use. Mutliple intensity- and shape based features can be calculated from detected objects (e.g. texture: haralick, Garbor, SER). Objects can be classified by these features by using hard thresholds or by supervised machine learning. Analysis workflows and results are stored in the database and can be exprted as csv tables for secondary analysis. Simple secondary analysis workflows can be also applied in Columbus directly. Results can be visualized as heatmaps on the plate view. The HCS statistics software Genedata Screener Assay Analyzer can be directly connected to the database.

Columbus screenshot

Matlab Computer Vision System Toolbox

Collection
Description
The Matlab Computer Vision System Toolbox extends the Matlab core functionality with general purpose image processing functions for feature detection & extraction, object detection & tracking and motion estimation. Strengths: - Most functions extend to nD - optimized functions (muti-threaded for some) - Matlab community (Matlab central) - relatively low entry-threshold for functionality - Tutorials & Webinars Limitations: - no embedded visualization of nD Microscopy data
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Matlab Image Processing Toolbox

Collection
Description
The Matlab image processing toolbox extends the Matlab core functionality with general purpose image processing capabilities. This ranges from image access (read / write), common filters (convolution, morphology, order based, Wiener, feature extraction, image enhancement, ...), image transformation (rotation, affine transformation, ...) to segmentation algorithms (thresholding, watershed, region growing). There is also an extensive list of functions to deal with binary or label mask and perform for instance connected particle analysis or morphological operations. Strengths: - Most functions extend to nD - optimized functions (muti-threaded for some) - Matlab community (Matlab central) - relatively low entry-threshold for functionality - Tutorials & Webinars Limitations: - no embedded visualization of nD Microscopy data
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Imaris

Collection
Description

Imaris is a software for data visualization, analysis, segmentation and interpretation of 3D and 4D microscopy images. It performs interactive volume rendering that lets users freely navigate even very large datasets (hundreds of GB). It performs both manual and automated detection and tracking of biological “objects” such as cells, nuclei, vesicles, neurons, and many more. ImarisSpots for example is a tool to detect “spherical objects” and track them in time series. Besides the automated detection it gives the user the ability to manually delete and place new spots in 3D space. ImarisCell is a tool to detect nuclei, cell boundaries and vesicles and track these through time. ImarisFilament is a module that lets users trace neurons and detect spines. For any detected object Imaris computes a large set of statistics values such as volume, surface area, maximum intensity of first channel, number of vesicles per cell etc. These values can be exported to Excel and statistics software packages. The measurements can also be analyzed directly within ImarisVantage which is a statistics tool that provides the link back to the 3D objects and the original image data. Strengths: - good visualization - user friendly interface - reads most microscopy file formats - image analysis workflows are very easy to apply - interactive editing of objects to correct errors during automatic detection - large data visualization (hundreds of GB)

has topic
Multimodal imaging
has function
Image registration
Object detection
Object tracking
Visualisation
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Definiens Developer

Collection
Description
Definiens is a commercial image segmentation and classification tool. The user designs a signal processing workflow by combining built-in filtering, thresholding and object classification modules. Object detection is typically done on hierarchical object levels, e.g cell level for cell objects and organelle level Nucleus and ER obejcts inside a cell object. For each object, a huge set of features (shape-based, intensity based, relations to neighbor objects...) is available and can be used for object classification or merging with neighboring objects. The classical definiens workflow is the so called bottom-up approach: In a first step, the image is segmented in numerous small objects, resulting in a heavy oversegmentation of of the target objects. Objects are then fused step by step on basis of features like “relative border to neighbor object” or “elliptic fit of resulting (fused) object”. Objects can assigned to different classes (like “nucleus” or “cancer cell”), based on their features. Weaknesses: -complex to use -closed (no API) -very expensive -relatively slow (you have to buy one license for each core) -bad 3D-visualization -time lapse analysis is possible but complicated Strengths: -powerful method to classify objects based on multiple features -2D data, especially histological data -good training material to learn software usage -detailed documentation
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