Drosophila melanogaster



Collection of several basic standard image segmentation methods focusing on medical imaging. In particular, the key block/applications are (un)supervised image segmentation using superpixels, object centre detection and region growing with a shape prior. Besides the open-source code, there is also a few sample images.


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Submitted by sampaio on Tue, 02/05/2019 - 10:12

Five different samples of Drosophila melanogaster Kc167 cells were stained with Hoechst 33342, a DNA stain. The last sample (labeled nodsRNA) is of wild-type cells. Each of the other four samples (labeled 48, 340, Anillin, and mad2) has a different gene knocked down by RNAi.

The sample preparation is described in more detail by [Carpenter et al. (Genome Biology, 2006)(http://genomebiology.biomedcentral.com/articles/10.1186/gb-2006-7-10-r1…)

Microscope autopilot


AutoPilot is the open source project that hosts the general algorithm for fast and robust assessment of local image quality, an automated computational method for image-based mapping of the three-dimensional light-sheet geometry inside a fluorescently labeled biological specimen, and a general algorithm for data-driven optimization of the system state of light-sheet microscopes capable of multi-color imaging with multiple illumination and detection arms.

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APP (All-path pruning)


"We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal- covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly)"

This plugin can be used with default parameters or user-defined parameters.


idTracker: Tracking animals


idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

Video protocol: https://www.youtube.com/watch?v=oC9tp5TKAyw

Example image: Example video of 5 zebrafish

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Microtubule end tracking in Drosophila Oocyte


Microtubule end tracking in live cell fluorescent images of Drosophila oocyte involves overcoming the following challenges, which can be tackled by a series of preprocessing steps and tracking described in Parton et al (2011)

  • illumination flicker & photobleaching: suppress by normalising intensities, e.g. using Image->Adjust->Bleach Correction in Fiji/ImageJ
  • uneven illumination: Fourier bandpass filtering (e.g. Process->FFT->Bandpass Filter) preserves features within a selected size range
  • high background / poor contrast: foreground filter, e.g. Temporal Median filter
  • tracking: e.g. TrackMate in Fiji/ImageJ (segmentation using DoG detector)
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Tissue Cell Segmentation


This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).

Sample image data is available in the documentation page.