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.
%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 = {2024-11-22T15:45:41.000+0100},
title = {{SpheroScan}: a user-friendly deep learning tool for spheroid image analysis},
volume = 12,
year = 2022
}
