Araştırma Makalesi
BibTex RIS Kaynak Göster

Kidney Segmentations Using CNN models

Yıl 2023, Cilt: 4 Sayı: 1, 1 - 13, 24.06.2023

Mohammed Mansour Mert Süleyman Demirsoy Mustafa Çağrı Kutlu

https://doi.org/10.58769/joinssr.1175622
Cited By: 3

Öz

For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program (HuBMAP). The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to solve the issue. The high size images were decoded to be fitted and trained in the models first, then the CNN models were trained. The CNN models result show that the Efficient Net has the highest accuracy rate with 99.49 %, and Mobile Net with 99.33 %.

Anahtar Kelimeler

kideny segmeantion CNN

Kaynakça

  • “What is HuBMAP”, HuBMAP, Accessed in 10.04.2021, Available [Online]: https://hubmapconsortium.org/what-is-hubmap.
  • “What Is Image Segmentation?”, MathWorks, Accessed in 11.06.2021, Available [Online]: https://www.mathworks.com/discovery/image-segmentation.html.
  • B. De Brabandere, D. Neven, L. Van Gool, “Semantic instance segmentation for autonomous driving”, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops, 2017.
  • R. I. Haque, J. Neubert, “Deep learning approaches to biomedical image segmentation”, Informatics in Medicine Unlocked, 2020.
  • J. Song, L. Xiao, M. Molaei, Z. Lian, “Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images”, Knowledge-Based Systems, vol. 176, pp. 40 – 53, 2019.
  • S. M. Sheehan, & R. Korstanje, “Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning”, American Journal of Physiology-Renal Physiology, vol. 315, no. 6, pp. 1644-1651, 2018.
  • J. Gallego, A, Pedraza, S. Lopez, G. Steiner, L. Gonzalez, A. Laurinavicius, & G. Bueno, “Glomerulus classification and detection based on convolutional neural networks”, Journal of Imaging, vol. 4, no. 1, pp. 20, 2018.
  • J. D. Bukowy, A. Dayton, D. Cloutier, A. D. Manis, A. Staruschenko, J. H. Lombard & A. W. Cowley, “Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections”, Journal of the American Society of Nephrology, vol. 29, no. 8, pp.2081-2088, 2018.
  • S. Kannan, L. A. Morgan, B. Liang, M. G. Cheung, C. Q. Lin, D. Mun, & V. B. “Kolachalama, Segmentation of glomeruli within trichrome images using deep learning”, Kidney international reports, vol. 4, no. 7, pp. 955-962, 2019.
  • N. Altini, G. D. Cascarano, A. Brunetti, F. Marino, M. T. Rocchetti, S. Matino, & V. “Bevilacqua, Semantic segmentation framework for glomeruli detection and classification in kidney histological sections”, Electronics, vol. 9, no. 3, pp. 503, 2020.
  • Bueno, M. M. Fernandez-Carrobles, L. Gonzalez-Lopez, & O Deniz, “Glomerulosclerosis identification in whole slide images using semantic segmentation”, Computer Methods and Programs in Biomedicine, vol. 184, 2020.
  • “Data Description”, HuBMAP - Hacking the Kidney, Accessed in 10.04.2021, Available [Online]: https://www.kaggle.com/c/hubmap-kidney-segmentation/data
  • N. R. Pal, S. K. Pal, “A review on image segmentation techniques”, Pattern Recognition, vol. 26, no. 9, pp. 1277 – 1294, 1993.
  • N. M. Zaitoun, M. J. Aqel, “Survey on image segmentation techniques”, Procedia Computer Science, vol. 65, pp. 797 – 806, International Conference on Communications, management, and Information technology (ICCMIT’2015), 2015.
  • D. Zhang, Y. Song, D. Liu, H. Jia, S. Liu, Y. Xia, H. Huang, W. Cai, “Panoptic segmentation with an end-to-end cell r-cnn for pathology image analysis”, pp. 237–244, 2018.
  • “Why MobileNet?” ProgrammerSought, Accessed in 13.06.2021, Available [Online]: https://www.programmersought.com/article/68608226299/
  • M. Tan, & Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks”, In International Conference on Machine Learning, pp. 6105-6114. PMLR, 2019.
  • G. L. W. Yong, & G. Lemaitre, “Evaluation measures for segmentation”, Matrix, vol. 1, no. 1, pp. 2, 2016.

Yıl 2023, Cilt: 4 Sayı: 1, 1 - 13, 24.06.2023

Mohammed Mansour Mert Süleyman Demirsoy Mustafa Çağrı Kutlu

https://doi.org/10.58769/joinssr.1175622
Cited By: 3

Öz

Kaynakça

  • “What is HuBMAP”, HuBMAP, Accessed in 10.04.2021, Available [Online]: https://hubmapconsortium.org/what-is-hubmap.
  • “What Is Image Segmentation?”, MathWorks, Accessed in 11.06.2021, Available [Online]: https://www.mathworks.com/discovery/image-segmentation.html.
  • B. De Brabandere, D. Neven, L. Van Gool, “Semantic instance segmentation for autonomous driving”, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops, 2017.
  • R. I. Haque, J. Neubert, “Deep learning approaches to biomedical image segmentation”, Informatics in Medicine Unlocked, 2020.
  • J. Song, L. Xiao, M. Molaei, Z. Lian, “Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images”, Knowledge-Based Systems, vol. 176, pp. 40 – 53, 2019.
  • S. M. Sheehan, & R. Korstanje, “Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning”, American Journal of Physiology-Renal Physiology, vol. 315, no. 6, pp. 1644-1651, 2018.
  • J. Gallego, A, Pedraza, S. Lopez, G. Steiner, L. Gonzalez, A. Laurinavicius, & G. Bueno, “Glomerulus classification and detection based on convolutional neural networks”, Journal of Imaging, vol. 4, no. 1, pp. 20, 2018.
  • J. D. Bukowy, A. Dayton, D. Cloutier, A. D. Manis, A. Staruschenko, J. H. Lombard & A. W. Cowley, “Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections”, Journal of the American Society of Nephrology, vol. 29, no. 8, pp.2081-2088, 2018.
  • S. Kannan, L. A. Morgan, B. Liang, M. G. Cheung, C. Q. Lin, D. Mun, & V. B. “Kolachalama, Segmentation of glomeruli within trichrome images using deep learning”, Kidney international reports, vol. 4, no. 7, pp. 955-962, 2019.
  • N. Altini, G. D. Cascarano, A. Brunetti, F. Marino, M. T. Rocchetti, S. Matino, & V. “Bevilacqua, Semantic segmentation framework for glomeruli detection and classification in kidney histological sections”, Electronics, vol. 9, no. 3, pp. 503, 2020.
  • Bueno, M. M. Fernandez-Carrobles, L. Gonzalez-Lopez, & O Deniz, “Glomerulosclerosis identification in whole slide images using semantic segmentation”, Computer Methods and Programs in Biomedicine, vol. 184, 2020.
  • “Data Description”, HuBMAP - Hacking the Kidney, Accessed in 10.04.2021, Available [Online]: https://www.kaggle.com/c/hubmap-kidney-segmentation/data
  • N. R. Pal, S. K. Pal, “A review on image segmentation techniques”, Pattern Recognition, vol. 26, no. 9, pp. 1277 – 1294, 1993.
  • N. M. Zaitoun, M. J. Aqel, “Survey on image segmentation techniques”, Procedia Computer Science, vol. 65, pp. 797 – 806, International Conference on Communications, management, and Information technology (ICCMIT’2015), 2015.
  • D. Zhang, Y. Song, D. Liu, H. Jia, S. Liu, Y. Xia, H. Huang, W. Cai, “Panoptic segmentation with an end-to-end cell r-cnn for pathology image analysis”, pp. 237–244, 2018.
  • “Why MobileNet?” ProgrammerSought, Accessed in 13.06.2021, Available [Online]: https://www.programmersought.com/article/68608226299/
  • M. Tan, & Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks”, In International Conference on Machine Learning, pp. 6105-6114. PMLR, 2019.
  • G. L. W. Yong, & G. Lemaitre, “Evaluation measures for segmentation”, Matrix, vol. 1, no. 1, pp. 2, 2016.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Araştırma Makaleleri
Yazarlar

Mohammed Mansour

Mert Süleyman Demirsoy

Mustafa Çağrı Kutlu 0000-0003-1663-2523

Yayımlanma Tarihi 24 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 4 Sayı: 1

Kaynak Göster

APA Mansour, M., Demirsoy, M. S., & Kutlu, M. Ç. (2023). Kidney Segmentations Using CNN models. Journal of Smart Systems Research, 4(1), 1-13. https://doi.org/10.58769/joinssr.1175622
AMA Mansour M, Demirsoy MS, Kutlu MÇ. Kidney Segmentations Using CNN models. JoinSSR. Haziran 2023;4(1):1-13. doi:10.58769/joinssr.1175622
Chicago Mansour, Mohammed, Mert Süleyman Demirsoy, ve Mustafa Çağrı Kutlu. “Kidney Segmentations Using CNN Models”. Journal of Smart Systems Research 4, sy. 1 (Haziran 2023): 1-13. https://doi.org/10.58769/joinssr.1175622.
EndNote Mansour M, Demirsoy MS, Kutlu MÇ (01 Haziran 2023) Kidney Segmentations Using CNN models. Journal of Smart Systems Research 4 1 1–13.
IEEE M. Mansour, M. S. Demirsoy, ve M. Ç. Kutlu, “Kidney Segmentations Using CNN models”, JoinSSR, c. 4, sy. 1, ss. 1–13, 2023, doi: 10.58769/joinssr.1175622.
ISNAD Mansour, Mohammed vd. “Kidney Segmentations Using CNN Models”. Journal of Smart Systems Research 4/1 (Haziran 2023), 1-13. https://doi.org/10.58769/joinssr.1175622.
JAMA Mansour M, Demirsoy MS, Kutlu MÇ. Kidney Segmentations Using CNN models. JoinSSR. 2023;4:1–13.
MLA Mansour, Mohammed vd. “Kidney Segmentations Using CNN Models”. Journal of Smart Systems Research, c. 4, sy. 1, 2023, ss. 1-13, doi:10.58769/joinssr.1175622.
Vancouver Mansour M, Demirsoy MS, Kutlu MÇ. Kidney Segmentations Using CNN models. JoinSSR. 2023;4(1):1-13.

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