SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis

Background

In recent years, three-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 two-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<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</ext-link>.

Key Points

• A deep learning model was trained to detect and segment spheroids in images from microscopes and Incucytes.

• The model performed well on both types of images with the total loss decreasing significantly during the training process.

• A web tool called SpheroScan was developed to facilitate the analysis of spheroid images, which includes prediction and visualization modules.

• SpheroScan is efficient and scalable, making it possible to handle large datasets with ease.

• SpheroScan is user-friendly and accessible to researchers, making it a valuable resource for the analysis of spheroid image data.