Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912466999443456 |
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| author | Li, Yuzhu Pillar, Nir Liu, Tairan Ma, Guangdong Qi, Yuxuan de Haan, Kevin Zhang, Yijie Yang, Xilin Correa, Adrian J. Xiao, Guangqian Jen, Kuang-Yu Iczkowski, Kenneth A. Wu, Yulun Wallace, William Dean Ozcan, Aydogan |
| author_facet | Li, Yuzhu Pillar, Nir Liu, Tairan Ma, Guangdong Qi, Yuxuan de Haan, Kevin Zhang, Yijie Yang, Xilin Correa, Adrian J. Xiao, Guangqian Jen, Kuang-Yu Iczkowski, Kenneth A. Wu, Yulun Wallace, William Dean Ozcan, Aydogan |
| contents | Organ transplantation serves as the primary therapeutic strategy for end-stage organ failures. However, allograft rejection is a common complication of organ transplantation. Histological assessment is essential for the timely detection and diagnosis of transplant rejection and remains the gold standard. Nevertheless, the traditional histochemical staining process is time-consuming, costly, and labor-intensive. Here, we present a panel of virtual staining neural networks for lung and heart transplant biopsies, which digitally convert autofluorescence microscopic images of label-free tissue sections into their brightfield histologically stained counterparts, bypassing the traditional histochemical staining process. Specifically, we virtually generated Hematoxylin and Eosin (H&E), Masson's Trichrome (MT), and Elastic Verhoeff-Van Gieson (EVG) stains for label-free transplant lung tissue, along with H&E and MT stains for label-free transplant heart tissue. Subsequent blind evaluations conducted by three board-certified pathologists have confirmed that the virtual staining networks consistently produce high-quality histology images with high color uniformity, closely resembling their well-stained histochemical counterparts across various tissue features. The use of virtually stained images for the evaluation of transplant biopsies achieved comparable diagnostic outcomes to those obtained via traditional histochemical staining, with a concordance rate of 82.4% for lung samples and 91.7% for heart samples. Moreover, virtual staining models create multiple stains from the same autofluorescence input, eliminating structural mismatches observed between adjacent sections stained in the traditional workflow, while also saving tissue, expert time, and staining costs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_05255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining Li, Yuzhu Pillar, Nir Liu, Tairan Ma, Guangdong Qi, Yuxuan de Haan, Kevin Zhang, Yijie Yang, Xilin Correa, Adrian J. Xiao, Guangqian Jen, Kuang-Yu Iczkowski, Kenneth A. Wu, Yulun Wallace, William Dean Ozcan, Aydogan Medical Physics Computer Vision and Pattern Recognition Machine Learning Organ transplantation serves as the primary therapeutic strategy for end-stage organ failures. However, allograft rejection is a common complication of organ transplantation. Histological assessment is essential for the timely detection and diagnosis of transplant rejection and remains the gold standard. Nevertheless, the traditional histochemical staining process is time-consuming, costly, and labor-intensive. Here, we present a panel of virtual staining neural networks for lung and heart transplant biopsies, which digitally convert autofluorescence microscopic images of label-free tissue sections into their brightfield histologically stained counterparts, bypassing the traditional histochemical staining process. Specifically, we virtually generated Hematoxylin and Eosin (H&E), Masson's Trichrome (MT), and Elastic Verhoeff-Van Gieson (EVG) stains for label-free transplant lung tissue, along with H&E and MT stains for label-free transplant heart tissue. Subsequent blind evaluations conducted by three board-certified pathologists have confirmed that the virtual staining networks consistently produce high-quality histology images with high color uniformity, closely resembling their well-stained histochemical counterparts across various tissue features. The use of virtually stained images for the evaluation of transplant biopsies achieved comparable diagnostic outcomes to those obtained via traditional histochemical staining, with a concordance rate of 82.4% for lung samples and 91.7% for heart samples. Moreover, virtual staining models create multiple stains from the same autofluorescence input, eliminating structural mismatches observed between adjacent sections stained in the traditional workflow, while also saving tissue, expert time, and staining costs. |
| title | Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining |
| topic | Medical Physics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.05255 |