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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.09601 |
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| _version_ | 1866929589100478464 |
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| author | Pi, Shaohua Ganjee, Razieh Wang, Lingyun Arbuckle, Riley K. Zhao, Chengcheng Sahel, Jose A Wang, Bingjie Chen, Yuanyuan |
| author_facet | Pi, Shaohua Ganjee, Razieh Wang, Lingyun Arbuckle, Riley K. Zhao, Chengcheng Sahel, Jose A Wang, Bingjie Chen, Yuanyuan |
| contents | This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09601 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fully Automated OCT-based Tissue Screening System Pi, Shaohua Ganjee, Razieh Wang, Lingyun Arbuckle, Riley K. Zhao, Chengcheng Sahel, Jose A Wang, Bingjie Chen, Yuanyuan Medical Physics Computer Vision and Pattern Recognition This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields. |
| title | Fully Automated OCT-based Tissue Screening System |
| topic | Medical Physics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.09601 |