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fastER / fast segmentation with Extremal Regions
Permits cell segmentation based on extremal regions. fastER extracts texture and shape features from candidate regions and estimates the likelihood of each to be a cell with a support vector machine (SVM). It uses a divide and conquer approach to calculate an optimal set of non-overlapping candidate regions. The tool allows fast and accurate automated segmentation in large volumes of data and enables on the fly analysis of running experiments.
DISCO / Data Informed Segmentation of Cell Objects
Segments and tracks budding yeast cells. DISCO is a comprehensive framework structured into several stages for integrated identification, segmentation and tracking of cells: (i) identification of physical features of the microfluidic device, (ii) supervised classification to identify cell centers, (iii) segmentation using a morphologically constrained cell-shape model, (iv) incorporation of temporal information to refine cell center prediction and (v) iterative greedy optimization of cell contours.
MicrobeTracker
Enables cell lineage tracking. MicrobeTracker utilizes cell shape and timelapse information to achieve cell outlining. It can track fluorescently labeled molecules in cell lineages over several generations or in difficult-to-resolve samples, such as densely-packed or filamentous cells, from time-lapse sequences. This tool is delivered with an accessory tool, called SpotFinder, that detects small round spots, generating precise cell coordinates of fluorescently labeled foci inside cells.
FogBank
An automated segmentation method that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.
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