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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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
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
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
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
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
Daniels N (2001) Justice, health, and healthcare. Am J Bioeth 1(2):2–16
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
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
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
Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vision, Graphics, Image Process 29(1):100–132
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
Jais IKM, Ismail AR, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):41–46
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
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
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
Kimmelstiel P, Wilson C (1936) Intercapillary lesions in the glomeruli of the kidney. Am J Pathol 12(1):83
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
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
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
Ramos D, Franco-Pedroso J, Lozano-Diez A, Gonzalez-Rodriguez J (2018) Deconstructing cross-entropy for probabilistic binary classifiers. Entropy 20(3):208
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
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
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
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
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
Von Lubitz D, Wickramasinghe N (2006) Healthcare and technology: the doctrine of networkcentric healthcare. Int J Electron Healthc 2(4):322–344
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
Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern Recogn 29(8):1335–1346
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
This research was financially supported by the Ministry of Education Malaysia under FRGS Grant (No.: FRGS/1/2018/STG06/UPM/02/6).
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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
- Deep learning
- Dice coefficient
- Human kidney
- Image segmentation
- PAS-stained kidney images