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Identify glomeruli in human kidney tissue images using a deep learning approach

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Abstract

Healthcare is the most important need of today’s era. Healthcare refers to the improvement of the human health by preventing, curing, diagnosing, recovering from a health hazard caused. Thus, to improve the health condition of a human system technology, such as machine learning, deep learning and artificial intelligence has come into play. The combination of artificial technology with the health sector has made a huge impact and success on the world. Curing millions of diseases, analysis of various infections, providing accurate test results and high-level maintenance check are now all possible with the evolution of technology. Every part of human body can now be diagnosed and analyze to study all kinds of tissues, blood vessels, organs, cells for improvement of health and curing of diseases. Research sector has been working with a continuous pace to accomplish various studies to identify different body organs and have a descriptive study for the identification of proper working mechanism of the human body. One such study has also shown a huge progress in the recent times, the identification of glomeruli in human kidney tissue. The tiny ball like structured which is composed of blood vessels that has an actively participation in the filtration of the blood to form urine. Thus, the paper presents a deep learning-based model formed for the identification of these glomeruli present in the human kidney. After implementing, the proposed model obtained an accuracy of 99.68% with a dice coefficient of 0.9060.

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References

  • Abraham N, & Khan NM (2019). A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 683–687). IEEE.

  • Altini N, Cascarano GD, Brunetti A, Marino F, Rocchetti MT, Matino S, Bevilacqua V (2020) Semantic segmentation framework for glomeruli detection and classification in kidney histological sections. Electronics 9(3):503

    Article  Google Scholar 

  • Ayyar M, Mathur P, Shah RR, & Sharma SG (2018) Harnessing ai for kidney glomeruli classification. In 2018 IEEE International Symposium on Multimedia (ISM) (pp. 17–20). IEEE.

  • Bouteldja N, Klinkhammer BM, Bülow RD, Droste P, Otten SW, von Stillfried SF, Merhof D (2021) Deep learning-based segmentation and quantification in experimental kidney histopathology. J Am Soc Nephrol 32(1):52–68

    Article  Google Scholar 

  • Bueno G, Fernandez-Carrobles MM, Gonzalez-Lopez L, Deniz O (2020) Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput Methods Progr Biomed 184:105273

    Article  Google Scholar 

  • Chagas P, Souza L, Araújo I, Aldeman N, Duarte A, Angelo M, Oliveira L (2020) Classification of glomerular hypercellularity using convolutional features and support vector machine. Artif Intell Med 103:101808

    Article  Google Scholar 

  • Cigizoglu HK, Kişi Ö (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Hydrol Res 36(1):49–64

    Article  Google Scholar 

  • Daniels N (2001) Justice, health, and healthcare. Am J Bioeth 1(2):2–16

    Article  Google Scholar 

  • Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Ronneberger O (2019) U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16(1):67–70

    Article  Google Scholar 

  • Gallego J, Pedraza A, Lopez S, Steiner G, Gonzalez L, Laurinavicius A, Bueno G (2018) Glomerulus classification and detection based on convolutional neural networks. J Imaging 4(1):20

    Article  Google Scholar 

  • Ginley B, Tomaszewski JE, Yacoub R, Chen F, Sarder P (2017) Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology. J Med Imaging 4(2):021102

    Article  Google Scholar 

  • Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vision, Graphics, Image Process 29(1):100–132

    Article  Google Scholar 

  • Hermsen M, de Bel T, Den Boer M, Steenbergen EJ, Kers J, Florquin S, van der Laak JA (2019) Deep learning–based histopathologic assessment of kidney tissue. J Am Soc Nephrol 30(10):1968–1979

    Article  Google Scholar 

  • Jais IKM, Ismail AR, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):41–46

    Article  Google Scholar 

  • Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Lin JJ (2021) Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int 99(1):86–101

    Article  Google Scholar 

  • Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Kolachalama VB (2019) Segmentation of glomeruli within trichrome images using deep learning. Kidney Int Rep 4(7):955–962

    Article  Google Scholar 

  • Kim H, Ko Y, Jung KH (1993) Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems. IEEE Trans Power Delivery 8(3):1356–1366

    Article  Google Scholar 

  • Kimmelstiel P, Wilson C (1936) Intercapillary lesions in the glomeruli of the kidney. Am J Pathol 12(1):83

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674

    Article  Google Scholar 

  • Marée R, Dallongeville S, Olivo-Marin JC, & Meas-Yedid V (2016) An approach for detection of glomeruli in multisite digital pathology. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 1033–1036). IEEE.

  • Marsh JN, Matlock MK, Kudose S, Liu TC, Stappenbeck TS, Gaut JP, Swamidass SJ (2018) Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans Med Imaging 37(12):2718–2728

    Article  Google Scholar 

  • Ramos D, Franco-Pedroso J, Lozano-Diez A, Gonzalez-Rodriguez J (2018) Deconstructing cross-entropy for probabilistic binary classifiers. Entropy 20(3):208

    Article  Google Scholar 

  • Schell C, Wanner N, Huber TB (2014) Glomerular development–shaping the multi-cellular filtration unit. Seminars in cell & developmental biology, vol 36. Academic Press, Cambridge, pp 39–49

    Google Scholar 

  • Shamir RR, Duchin Y, Kim J, Sapiro G, & Harel N (2019) Continuous dice coefficient: a method for evaluating probabilistic segmentations. arXiv preprint.

  • Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  • Simon O, Yacoub R, Jain S, Tomaszewski JE, Sarder P (2018) Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images. Sci Rep 8(1):1–11

    Article  Google Scholar 

  • Smith JM, Conroy RM (2018) The NIH common fund Human Biomolecular Atlas Program (HuBMAP): Building a framework for mapping the human body. FASEB J 32:818–822

    Article  Google Scholar 

  • American Joint Committee on Cancer. (2002). Kidney. In AJCC cancer staging manual (pp. 323–328). Springer, New York, NY.

  • Sun H, Chen X, Shi Q, Hong M, Fu X, & Sidiropoulos ND (2017). Learning to optimize: Training deep neural networks for wireless resource management. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1–6). IEEE.

  • Tan M, & Le Q (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105–6114). PMLR.

  • Uchino E, Suzuki K, Sato N, Kojima R, Tamada Y, Hiragi S, Okuno Y (2020) Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach. Int J Med Inf 141:104231

    Article  Google Scholar 

  • Von Lubitz D, Wickramasinghe N (2006) Healthcare and technology: the doctrine of networkcentric healthcare. Int J Electron Healthc 2(4):322–344

    Article  Google Scholar 

  • Zeng C, Nan Y, Xu F, Lei Q, Li F, Chen T, Liu Z (2020) Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning. J Pathol 252(1):53–64

    Article  Google Scholar 

  • Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern Recogn 29(8):1335–1346

    Article  Google Scholar 

  • Zhang P, Yang L, Li D (2020) EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment. Comput Electron Agric 176:105652

    Article  Google Scholar 

Download references

Funding

This research was financially supported by the Ministry of Education Malaysia under FRGS Grant (No.: FRGS/1/2018/STG06/UPM/02/6).

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Shubham Shubham, Nikita Jain & Vedika Gupta

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Senthilkumar Mohan

  3. Positive Computing Research Cluster, Universiti Teknologi Petronas, 32610, Seri Iskandar, Perak, Malaysia

    Mazeyanti Mohd Ariffin

  4. Institute of IR 4.0, The National University of Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    Ali Ahmadian

  5. Department of Mathematics, Near East University, Nicosia, TRNC, Mersin 10, Turkey

    Ali Ahmadian

  6. Institute for Mathematical Research, Universiti Putra Malaysia,, 43600 UPM, Serdang, Malaysia

    Ali Ahmadian

Authors
  1. Shubham Shubham
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  2. Nikita Jain
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  3. Vedika Gupta
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  4. Senthilkumar Mohan
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  5. Mazeyanti Mohd Ariffin
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  6. Ali Ahmadian
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All authors have equally contributed towards the formation of this paper.

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Correspondence to Ali Ahmadian.

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The authors declare that they have no competing interests.

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Communicated by Oscar Sanjuán Martínez.

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Shubham, S., Jain, N., Gupta, V. et al. Identify glomeruli in human kidney tissue images using a deep learning approach. Soft Comput 27, 2705–2716 (2023). https://doi.org/10.1007/s00500-021-06143-z

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