Automated Cell Nuclei detection for Large-Volume Electron Microscopy of Neural Tissue

Volumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their onnections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here.

Drosophila Larvae Tracking

The task associated to this dataset is tracking multiple drosophila larvae. Such a tracking is required in the quest to elucidate the genetic basis of Drosophila's behaviour. This dataset was used in the article "Tracking indistinguishable translucent objects over time using weakly supervised structured learning". We provide the raw data, an intermediate segmentation of the foreground and the gold standard used in the evaluation of that tracking algorithm.

Drosophila Cell Tracking

This 3D+t dataset shows an excerpt of a developing fruit fly (Drosophila melanogaster) embryo over 100 time steps during gastrulation. The movie has been recorded using a light sheet microscope. We provide dense manual annotations for cell lineages for all ~800 cells per frame.