Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning

Autor: Isil, Cagatay, Koydemir, Hatice Ceylan, Eryilmaz, Merve, de Haan, Kevin, Pillar, Nir, Mentesoglu, Koray, Unal, Aras Firat, Rivenson, Yair, Chandrasekaran, Sukantha, Garner, Omai B., Ozcan, Aydogan

Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable t

Externí odkaz: http://arxiv.org/abs/2407.12337

Roadmap on Deep Learning for Microscopy

Autor: Volpe, Giovanni, Wählby, Carolina, Tian, Lei, Hecht, Michael, Yakimovich, Artur, Monakhova, Kristina, Waller, Laura, Sbalzarini, Ivo F., Metzler, Christopher A., Xie, Mingyang, Zhang, Kevin, Lenton, Isaac C. D., Rubinsztein-Dunlop, Halina, Brunner, Daniel, Bai, Bijie, Ozcan, Aydogan, Midtvedt, Daniel, Wang, Hao, Sladoje, Nataša, Lindblad, Joakim, Smith, Jason T., Ochoa, Marien, Barroso, Margarida, Intes, Xavier, Qiu, Tong, Yu, Li-Yu, You, Sixian, Liu, Yongtao, Ziatdinov, Maxim A., Kalinin, Sergei V., Sheridan, Arlo, Manor, Uri, Nehme, Elias, Goldenberg, Ofri, Shechtman, Yoav, Moberg, Henrik K., Langhammer, Christoph, Špačková, Barbora, Helgadottir, Saga, Midtvedt, Benjamin, Argun, Aykut, Thalheim, Tobias, Cichos, Frank, Bo, Stefano, Hubatsch, Lars, Pineda, Jesus, Manzo, Carlo, Bachimanchi, Harshith, Selander, Erik, Homs-Corbera, Antoni, Fränzl, Martin, de Haan, Kevin, Rivenson, Yair, Korczak, Zofia, Adiels, Caroline Beck, Mijalkov, Mite, Veréb, Dániel, Chang, Yu-Wei, Pereira, Joana B., Matuszewski, Damian, Kylberg, Gustaf, Sintorn, Ida-Maria, Caicedo, Juan C., Cimini, Beth A, Bell, Muyinatu A. Lediju, Saraiva, Bruno M., Jacquemet, Guillaume, Henriques, Ricardo, Ouyang, Wei, Le, Trang, Gómez-de-Mariscal, Estibaliz, Sage, Daniel, Muñoz-Barrutia, Arrate, Lindqvist, Ebba Josefson, Bergman, Johanna

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural netw

Externí odkaz: http://arxiv.org/abs/2303.03793

Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

Autor: Zhang, Yijie, Huang, Luzhe, Liu, Tairan, Cheng, Keyi, de Haan, Kevin, Li, Yuzhu, Bai, Bijie, Ozcan, Aydogan

Publikováno v: Intelligent Computing (2022)

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining

Externí odkaz: http://arxiv.org/abs/2207.02946

Deep learning-based transformation of the H&E stain into special stains

Autor: de Haan, Kevin, Zhang, Yijie, Zuckerman, Jonathan E., Liu, Tairan, Sisk, Anthony E., Diaz, Miguel F. P., Jen, Kuang-Yu, Nobori, Alexander, Liou, Sofia, Zhang, Sarah, Riahi, Rana, Rivenson, Yair, Wallace, W. Dean, Ozcan, Aydogan

Publikováno v: Nature Communications (2021)

Pathology is practiced by visual inspection of histochemically stained slides. Most commonly, the hematoxylin and eosin (H&E) stain is used in the diagnostic workflow and it is the gold standard for cancer diagnosis. However, in many cases, especiall

Externí odkaz: http://arxiv.org/abs/2008.08871

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Autor: Zhang, Yijie, de Haan, Kevin, Rivenson, Yair, Li, Jingxi, Delis, Apostolos, Ozcan, Aydogan

Publikováno v: Light: Science & Applications (2020)

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However,

Externí odkaz: http://arxiv.org/abs/2001.07267