Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids
Frontiers in Cellular Neuroscience, ISSN: 1662-5102, Vol: 14, Page: 171
2020
- 42Citations
- 67Captures
- 4Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations42
- Citation Indexes42
- 42
- Captures67
- Readers67
- 67
- Mentions4
- News Mentions3
- News3
- Blog Mentions1
- Blog1
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Article Description
We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on bright-field imaging. The three-dimensional “organoid” approach for the differentiation of pluripotent stem cells (PSC) into retinal and other neural tissues has become a major in vitro strategy to recapitulate development. We decided to develop a universal, robust, and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic-contrast bright-field (BF) images contain sufficient information on tissue specification, and it is possible to extract this data using convolutional neural networks (CNNs). Retina-specific Rx-green fluorescent protein mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The BF images of organoids have been taken on day 5 and fluorescent on day 9. To train the CNN, we utilized a transfer learning approach: ImageNet pre-trained ResNet50v2, VGG19, Xception, and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best-performing classifier with ResNet50v2 architecture showed a receiver operating characteristic-area under the curve score of 0.91 on a test dataset. A comparison of the best-performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate (84% vs. 67 ± 6% of correct predictions, respectively), confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN’s ability to classify stem cell-derived tissue in vitro.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85088471539&origin=inward; http://dx.doi.org/10.3389/fncel.2020.00171; http://www.ncbi.nlm.nih.gov/pubmed/32719585; https://www.frontiersin.org/articles/10.3389/fncel.2020.00171/supplementary-material/10.3389/fncel.2020.00171.s001; http://dx.doi.org/10.3389/fncel.2020.00171.s001; https://www.frontiersin.org/article/10.3389/fncel.2020.00171/full; https://dx.doi.org/10.3389/fncel.2020.00171.s001; https://www.frontiersin.org/articles/10.3389/fncel.2020.00171/full; https://dx.doi.org/10.3389/fncel.2020.00171; https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2020.00171/full
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