Sample, test, and validation data for findmycells

findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells

Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.

For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:

Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gtht9d

The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)

Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. arXiv preprint arXiv:2111.06693.

The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".