Image segmentation

We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can“push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

EBImage is available through the Bioconductor software project (www.bioconductor.org). Strengths Lightweight Suitable for automated, scripted analyses All functions are documented with examples Modular links to R and Bioconductor software, notably imageHTS and cellHTS2 Community support via the Bioconductor mailing list Reproducible (image) analysis using the Sweave report-writing system

Workflow of data-analysis strategies for image-based cell profiling.

1. Image analysis (image correction, image segmentation, feature extraction)
2. Image quality control
3. Preprocessing extracted features
4. Dimensionality reduction
5. Single-cell data aggregation
6. Measuring profile similarity
7. Assay quality assessment
8. Downstream analysis

Examples of publications where the workflow has been used at least partly:

• Image-based multivariate profiling of drug responses from single cells
• Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes
• Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping
• Cell shape and the microenvironment regulate nuclear translocation of NF‐κB in breast epithelial and tumor cells
• Genome-wide RNAi Screening Identifies Protein Modules Required for 40S Subunit Synthesis in Human Cells
• Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
• Systematic morphological profiling of human gene and allele function via Cell Painting

Microscopy Image Browser (MIB) is a high-performance Matlab-based software package for advanced image processing, segmentation and visualization of multi-dimensional (2D-4D) light and electron microscopy datasets.

MIB is a freely available, user-friendly software for effective image processing of multidimensional datasets that improves and facilitates the full utilization of acquired data and enables quantitative analysis of morphological features. Its open-source environment enables fine tuning and possibility of adding new plug-ins to customize the program for specific needs of any research project.