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DropSnake is based on B-spline snakes (active contours) to shape and measure a drop.

FastSME: Faster and Smoother Manifold Extraction From 3D Stack.

3D image stacks are routinely acquired to capture data that lie on undulating 3D manifolds yet processed in 2D by biologists. Algorithms to reconstruct the specimen morphology into a 2D representation from the 3D image volume are employed in such scenarios. In this paper, we present FastSME, which offers several improvements on the baseline SME algorithm which enables accurate 2D representation of data on a manifold from 3D volumes, however is computationally expensive. The improvements are achieved in terms of processing speed (3X-10X speed-up depending on image size), minimizing sensitivity to initialization, and also increases local smoothness of the recovered manifold resulting in better reconstructed 2D composite image. We compare the proposed FastSME against the baseline SME as well as other accessible state-of-the-art tools on synthetic and real microscopy data. Our evaluation on multiple metrics demonstrates the efficiency of the presented method in maintaining fidelity of manifold shape and hence specimen morphology.

Smooth 2D Manifold Extraction (SME).

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

The research goal of this paper was to provide unbiased counts of labeled astrocytes and to estimate the area they cover, further to develop tools for defining the orientation of coupling within astrocyte networks under different stimuli.

In order to count the astrocytes and estimate the area they cover the following steps were used in this software.

Pre-processing: z-project (using max intensity); split channels; subtract background; remove outliers.

Segmentation: adjust threshold and convert to a binary file; Watershed.

Cell counting: Analyze particles

Measure Astrocytic network area: select a ROI using the polygon tool; set measurements (area); ROI manager -> add the traced polygon; measure.