How feasible is it to segment human glomerulus with a model trained on mouse histology images?
Resumo
Many genetic, physiological and structural characteristics of internal organs are shared by mice and humans. Hence, mice are frequently used in experimental model of human diseases. Although this is an indisputable truth in medicine, there is an avenue to go in computational pathology, where digital images are the main objects of investigation. Considering the lack of study about knowledge transfer between mice and humans concerning machine learning models, we propose investigating if it is possible to segment glomeruli in human WSIs by training deep learning models on mouse data only. Three different semantic segmenters were evaluated, which had their performance assessed on two data sets comprised of 18 mouse WSIs and 30 human WSIs. The results found corroborate our hypothesis validation.
Referências
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Justino Duarte Santos, Rodrigo de Melo Sousa Veras, Romuere Rodrigues Veloso, Nayze Lucena Sangreman Aldeman, Kelson Romulo Teixeira Aires, Andrea Gomes Campos Bianchi, et al. Classificação de imagens de biópsias renais com glomeruloesclerose segmentar e focal ou com lesões mınimas utilizando transfer learning em cnn. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 82–93. SBC, 2019.
Olivier Simon, Rabi Yacoub, Sanjay Jain, John E Tomaszewski, and Pinaki Sarder. Multiradial lbp features as a tool for rapid glomerular detection and assessment in whole slide histopathology images. Scientific reports, 8(1):1–11, 2018.
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Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019.
Ma Yi-de, Liu Qing, and Qian Zhi-Bai. Automated image segmentation using improved pcnn model based on cross-entropy. In Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004., pages 743–746. IEEE, 2004.
Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4413–4421, 2018.
Nassim Bouteldja, Barbara M Klinkhammer, Roman D Bülow, Patrick Droste, Simon W Otten, Saskia Freifrau von Stillfried, Julia Moellmann, Susan M Sheehan, Ron Korstanje, Sylvia Menzel, et al. Deep learning–based segmentation and quantification in experimental kidney histopathology. Journal of the American Society of Nephrology, 32(1):52–68, 2021.
Sara Chater, Nathan Lauzeral, Anass Nouri, Youssef El Merabet, and Florent Autrusseau. Learning from mouse ct-scan brain images to detect mra-tof human vasculatures. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2830–2834. IEEE, 2021.
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801–818, 2018.
Thomas de Bel, Meyke Hermsen, Bart Smeets, Luuk Hilbrands, Jeroen van der Laak, and Geert Litjens. Automatic segmentation of histopathological slides of renal tissue using deep learning. In Medical Imaging 2018: Digital Pathology, volume 10581, page 1058112. International Society for Optics and Photonics, 2018.
Tongle Fan, Guanglei Wang, Yan Li, and Hongrui Wang. Ma-net: A multi-scale attention network for liver and tumor segmentation. IEEE Access, 8:179656–179665, 2020.
Alton Brad Farris, Cynthia Cohen, Thomas E Rogers, and Geoffrey H Smith. Whole slide imaging for analytical anatomic pathology and telepathology: practical applications today, promises, and perils. Archives of pathology & laboratory medicine, 141(4): 542–550, 2017.
Michael Gadermayr, Ann-Kathrin Dombrowski, Barbara Mara Klinkhammer, Peter Boor, and Dorit Merhof. Cnn cascades for segmenting sparse objects in gigapixel whole slide images. Computerized Medical Imaging and Graphics, 71:40–48, 2019.
Brandon Ginley, Brendon Lutnick, Kuang-Yu Jen, Agnes B Fogo, Sanjay Jain, Avi Rosenberg, Vighnesh Walavalkar, Gregory Wilding, John E Tomaszewski, Rabi Yacoub, et al. Computational segmentation and classification of diabetic glomerulosclerosis. Journal of the American Society of Nephrology, 30(10):1953–1967, 2019.
Brandon Ginley, Kuang-Yu Jen, Avi Rosenberg, Felicia Yen, Sanjay Jain, Agnes Fogo, and Pinaki Sarder. Neural network segmentation of interstitial fibrosis, tubular atrophy, and glomerulosclerosis in renal biopsies. arXiv preprint arXiv:2002.12868, 2020.
Md Murad Hossain, Niloufar Saharkhiz, and Elisa E Konofagou. Feasibility of harmonic motion imaging using a single transducer: In vivo imaging of breast cancer in a mouse model and human subjects. IEEE Transactions on Medical Imaging, 40(5):1390–1404, 2021.
Lei Jiang,Wenkai Chen, Bao Dong, Ke Mei, Chuang Zhu, Jun Liu, Meishun Cai, Yu Yan, Gongwei Wang, Li Zuo, et al. A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images. The American Journal of Pathology, 191(8):1431–1441, 2021.
Hye-Ryoung Kim, Sung-Won Park, Hee-Jung Cho, Kyung-Ae Chae, Ji-Min Sung, Jin-Suk Kim, Christopher P Landowski, Duxin Sun, AM Abd El-Aty, Gordon L Amidon, et al. Comparative gene expression profiles of intestinal transporters in mice, rats and humans. Pharmacological research, 56(3):224–236, 2007.
Brendon Lutnick, Brandon Ginley, Darshana Govind, Sean D McGarry, Peter S LaViolette, Rabi Yacoub, Sanjay Jain, John E Tomaszewski, Kuang-Yu Jen, and Pinaki Sarder. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nature machine intelligence, 1(2):112–119, 2019.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3): 211–252, 2015.
Justino Duarte Santos, Rodrigo de Melo Sousa Veras, Romuere Rodrigues Veloso, Nayze Lucena Sangreman Aldeman, Kelson Romulo Teixeira Aires, Andrea Gomes Campos Bianchi, et al. Classificação de imagens de biópsias renais com glomeruloesclerose segmentar e focal ou com lesões mınimas utilizando transfer learning em cnn. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 82–93. SBC, 2019.
Olivier Simon, Rabi Yacoub, Sanjay Jain, John E Tomaszewski, and Pinaki Sarder. Multiradial lbp features as a tool for rapid glomerular detection and assessment in whole slide histopathology images. Scientific reports, 8(1):1–11, 2018.
Abigail L Smith and Dorcas J Corrow. Modifications to husbandry and housing conditions of laboratory rodents for improved well-being. ILAR journal, 46(2):140–147, 2005.
Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019.
Ma Yi-de, Liu Qing, and Qian Zhi-Bai. Automated image segmentation using improved pcnn model based on cross-entropy. In Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004., pages 743–746. IEEE, 2004.
Publicado
07/06/2022
Como Citar
SOUZA, Luiz; SILVA, Jefferson; CHAGAS, Paulo; DUARTE, Angelo; DOS-SANTOS, Washington LC; OLIVEIRA, Luciano.
How feasible is it to segment human glomerulus with a model trained on mouse histology images?. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 449-460.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222671.
