RESPAN: An Automated Pipeline for Accurate Dendritic Spine Mapping with Integrated Image Restoration.
Download the latest version of the RESPAN Windows executable here. This file is zipped using 7zip.
Please allow a minimum of 20GB of disk space for the software and ensure there is sufficient space for processing your data. RESPAN includes lossless compression of image files to ensure a minimal footprint for generated results and validation images. Pretrained models for a variety of image modalities are available for download here with addition information on each model noted below.| Segmentation Model | Modality | Resolution | Annotations | Details |
|---|---|---|---|---|
| Model 1 | Spinning disk and Airyscan/laser scanning confocal microscopy | 65 x 65 x 150nm | spines, dendrites, and soma | 112 datasets, including restored and raw data and additional augmentation |
| Model 2 | Spinning disk confocal microscopy | 65 x 65 x 65nm | spines, necks, dendrites, and soma | isotropic model, 7 datasets, no augmentation |
| Model 3 | Two-photon in vivo confocal microscopy | 102 x 102 x 1000nm | spines and dendrites | 908 datasets, additional augmentation |
For detailed protocols using RESPAN, please refer to our manuscript.
- Comprehensive Integration: RESPAN uniquely integrates image restoration, axial resolution enhancement, and deep learning-based segmentation into a single, user-friendly application.
- 3-Dimensional Analysis: 3D information is efficiently utilized at all stages of the pipeline, ensuring improved performance over approaches limited to 2D or a combination of 2D and 3D techniques for quantification.
- In Vivo Spine Tracking: RESPAN has been demonstrated to successfully track spines autonomously across time in 3D in challenging in vivo two-photon imaging conditions.
- Increased Accuracy: By enhancing image quality prior to segmentation, RESPAN significantly improves the accuracy of spine detection and morphological measurements.
- User-Friendly Deployment and Interface: A ready-to-run application with a graphical user interface allows users without programming skills to perform advanced analyses.
- Built-in Validation Tools: RESPAN includes tools for validating results against ground-truth data, promoting scientific rigor and reproducibility.
- Model Training: RESPAN includes tabs in the graphical user interface that allow training of CSBDeep, Self-Net, and nnU-Net models, which normally require separate environments using Tensorflow and PyTorch, removing a significant barrier to training and utilizing custom models.
If you use RESPAN as part of your research, please cite our work using the reference below:
Sergio B. Garcia, Alexa P. Schlotter, Daniela Pereira, Franck Polleux, Luke A. Hammond. (2024) RESPAN: An Automated Pipeline for Accurate Dendritic Spine Mapping with Integrated Image Restoration. bioRxiv. doi: https://doi.org/10.1101/2024.06.06.597812RESPAN has been used in the following publications:
- Baptiste Libé-Philippot, Ryohei Iwata, Aleksandra J. Recupero, Keimpe Wierda, Sergio Bernal Garcia, Luke Hammond, Anja van Benthem, Ridha Limame, Martyna Ditkowska, Sofie Beckers, Vaiva Gaspariunaite, Eugénie Peze-Heidsieck, Daan Remans, Cécile Charrier, Tom Theys, Franck Polleux, Pierre Vanderhaeghen (2024) Synaptic neoteny of human cortical neurons requires species-specific balancing of SRGAP2-SYNGAP1 cross-inhibition. Neuron. https://doi.org/10.1016/j.neuron.2024.08.021.
Updating soon with information on how to create the environments for running RESPAN directly in Python.