%0 Journal Article %1 Akshay2022-ad %A Akshay, Akshay %A Katoch, Mitali %A Abedi, Masoud %A Shekarchizadeh, Navid %A Besic, Mustafa %A Burkhard, Fiona C %A Bigger-Allen, Alex %A Adam, Rosalyn M %A Monastyrskaya, Katia %A Gheinani, Ali Hashemi %D 2022 %I Oxford University Press (OUP) %J Gigascience %K topic_federatedlearn 3D Image Mask R-CNN; analysis; deep high-throughput image learning; screening; segmentation spheroids; %T SpheroScan: a user-friendly deep learning tool for spheroid image analysis %V 12 %X 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.

@article{Akshay2022-ad, 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.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Akshay, Akshay and Katoch, Mitali and Abedi, Masoud and Shekarchizadeh, Navid and Besic, Mustafa and Burkhard, Fiona C and Bigger-Allen, Alex and Adam, Rosalyn M and Monastyrskaya, Katia and Gheinani, Ali Hashemi}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/288ca813db2fdfafd8d640ac6f0b423d0/scadsfct}, copyright = {https://creativecommons.org/licenses/by/4.0/}, interhash = {9b32723564fc9a0b624c2f4870258f6f}, intrahash = {88ca813db2fdfafd8d640ac6f0b423d0}, journal = {Gigascience}, keywords = {topic_federatedlearn 3D Image Mask R-CNN; analysis; deep high-throughput image learning; screening; segmentation spheroids;}, language = {en}, month = dec, publisher = {Oxford University Press (OUP)}, timestamp = {2024-11-28T17:41:03.000+0100}, title = {{SpheroScan}: a user-friendly deep learning tool for spheroid image analysis}, volume = 12, year = 2022 }