SpheroScan: a user-friendly deep learning tool for spheroid image analysis
Akshay A, Katoch M, Abedi M, Shekarchizadeh N, Besic M, Burkhard FC, Bigger-Allen A, Adam RM, Monastyrskaya K, Gheinani AH (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 12
Article Number: giad082
DOI: 10.1093/gigascience/giad082
Abstract
Background: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.
Authors with CRIS profile
Involved external institutions
Universität Bern
Switzerland (CH)
Universität Leipzig
Germany (DE)
Veterans Affairs Healthcare System Boston and Harvard Medical School
United States (USA) (US)
Boston Children's Hospital
United States (USA) (US)
Switzerland (CH)
Universität Leipzig
Germany (DE)
Veterans Affairs Healthcare System Boston and Harvard Medical School
United States (USA) (US)
Boston Children's Hospital
United States (USA) (US)
How to cite
APA:
Akshay, A., Katoch, M., Abedi, M., Shekarchizadeh, N., Besic, M., Burkhard, F.C.,... Gheinani, A.H. (2023). SpheroScan: a user-friendly deep learning tool for spheroid image analysis. GigaScience, 12. https://dx.doi.org/10.1093/gigascience/giad082
MLA:
Akshay, Akshay, et al. "SpheroScan: a user-friendly deep learning tool for spheroid image analysis." GigaScience 12 (2023).
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