New features in CP 2.0

From Cellprofiler Developer Wiki

A number of new features have been incorporated into this Python version of CellProfiler.

As we are still in Beta, there are of course some issues. These are listed on the CP_2.0_Beta_release page.

Interface

• Resizable user interface: The main CellProfiler interface can now be resized by dragging the window corner.
• Help for individual module settings: Every setting in every module now has a help button that you can click to display information and advice for that setting.
• Settings verification: CellProfiler constantly checks for setting values that are not allowed, and immediately flags them for you.
• Context-dependent module settings: Prior versions of CellProfiler displayed all settings for each module, whether or not the values were necessary, given existing choices for other settings. Now, only those settings you require are displayed, simplifying the interface
• Test mode for assay development: This feature allows you to preview the effect of a module setting on your data. You can step backward or forward in the pipeline as you modify settings, optimizing your results prior to running an actual analysis.
• Unlimited number of images/objects as module input: Some modules can accept an arbitrary number of images or objects as input, and you can dynamically add or remove any of these inputs as needed. For example, you can specify any number of single images in LoadSingleImage; previously, the module could accept only three input images at a time.
  For example, in OverlayOutlines, you can specify that any number of outlines be overlaid on an image; previously, you would have had to string multiple OverlayOutline modules together.
• Image grouping: Images which share common metadata tags, whether provided in the filename or in an accompanying text data file, can be processed together. This is useful for any situation in which the images are organized in groups and each group needs to be analyzed as an individual set, such as illumination correction for multiple plates.
• Module drag and drop: You can drag and drop selected modules within a pipeline or into another instance of CellProfiler, keeping their associated settings intact.
• Listing of recent pipelines: A selectable list of recently used pipelines is available from the menu bar, for easy access.
• Figure display choice: Easier access to which windows are displayed is now controlled within the pipeline, and is saved as part of the pipeline.
• Context menus: The module list responds to right-clicks, providing easy access to module manipulation or help.
• Error handling: This feature sends bug reports (stack traces) to our developers.
• Better access for developers: We are providing a developer's guide as a practical introduction for programming in the CellProfiler environment, an email list, and wiki, in addition to the available user forum.

Module improvements

• Improved Otsu thresholding: Choose two- or three-class thresholding to handle images where there might be an intermediate intensity level between foreground and background.
• Secondary object identification now permits discarding of objects touching the image border, along with the associated primary objects.
• Filtering objects by measurements now permits a set of objects to be filtered with any number of measurements.
• Masking of images/objects: You can create masks for use with both images and objects such that image/object measurements will include only those regions within the masked area.
• Improved loading of text information: Previously, you could load only a limited amount of annotation relevant to your images, via a text file. Now you can use comma-delimited files to load tables of metadata, in addition to file lists of input images for analysis.
• Convex hull has been included as an image morphological operation.
• A new module, MeasureNeurons, has been added, which measures the number of trunks and branches for each neuron in an image.
• Detection of new features: Neurites can be extracted from images of neurons. Branching points of line segments can be found as an image morphological operation. Also, "dark holes" (dark spots surrounded bright rings) can be detected.
• Improvements to object tracking: A new tracking algorithm has been added to the TrackObjects module which is capable of bridging temporal gaps in trajectories and accouting for splitting/merging events.
• Object data can be exported to a database as a single table containing all user-defined object measurements, or as separate tables, one for each object.
• SQLite support: Data can be exported in SQLite, a self-contained database format. Users can create their own local databases and no longer need access to a separate database server. Because CellProfiler Analyst also supports SQLite, any user can access CellProfiler Analyst's suite of data exploration and machine-leaning tools.