SEGMENTATION OF SCEROTIC AND NON-SCEROTIC RENAL BIOPSIES USING IMAGE J
DOI:
https://doi.org/10.29121/shodhkosh.v4.i2.2023.5559Keywords:
Imagej, Glomerular Sclerosis, Renal Pathol- Ogy, Image Segmentation, Computational Efficiency, Open-Source Software, Histopathological Quantification, Chronic Kidney Disease, Deep Learning Alternative, Mor- Phometric AnalysisAbstract [English]
Accurate identification and classifica- tion of glomeruli in renal biopsy specimens are fundamental for histopathological diagnosis and chronic kidney disease staging. While deep learning (DL) methodologies have advanced au- tomated segmentation, their reliance on exten- sive computational resources, large annotated datasets, and specialized expertise limits acces- sibility. This study introduces a standardized, open-source framework for glomerular segmenta- tion and sclerosis classification using ImageJ, cir- cumventing these barriers. Our pipeline inte- grates preprocessing, segmentation, and quantita- tive morphometric analysis to discriminate scle- rotic from non-sclerotic glomeruli based on struc- tural and textural biomarkers. The proposed method was validated using established perfor- mance metrics—Accuracy, Precision, Recall, and Intersection-over-Union (IoU)—on renal biopsy images. When benchmarked against contempo- rary DL-based segmentation techniques, our Im- ageJ workflow achieved comparable efficacy, while demonstrating superior computational efficiency, implementation simplicity, and methodological transparency. These results establish ImageJ as a practical, high-performance tool for glomeru- lar segmentation in renal pathology. The vali- dated workflow offers pathologists and researchers a resource-minimal, accessible alternative to com- putationally intensive DL systems, promoting scal- able adoption in clinical diagnostics and trans- lational research for objective histopathological quantification.
References
Harnessing AI for Kidney Glomeruli Classification
Gallego, J.; Pedraza, A.; Lopez, S.; Steiner, G.; Gonzalez, L.; Laurinavicius, A.; Bueno, G. Glomerulus classification and detection based on convolutional neural networks. J. Imaging 2018, 4, 20. [CrossRef]80.4 DOI: https://doi.org/10.3390/jimaging4010020
Cascarano, G.D.; Debitonto, F.S.; Lemma, R.; Brunetti, A.; Buongiorno, D.; De Feudis, I.; Guerriero, A.; Rossini, M.; Pesce, F.; Gesualdo, L.; et al. An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy; Springer: Berlin, Germany, 2019; pp. 727–738._66. [CrossRef]81.8 DOI: https://doi.org/10.1007/978-3-030-26766-7_66
Kawazoe, Y.; Shimamoto, K.; Yamaguchi, R.; Shintani-Domoto, Y.; Uozaki, H.; Fukayama, M.; Ohe, K. Faster R-CNN-based glomerular detection in multistained human whole slide images. J. Imaging 2018, 4, 91. [CrossRef]82.10 DOI: https://doi.org/10.3390/jimaging4070091
Ginley, B.; Lutnick, B.; Jen, K.Y.; Fogo, A.B.; Jain, S.; Rosenberg, A.; Walavalkar, V.; Wilding, G.; Tomaszewski, J.E.; Yacoub, R.; et al. Computational Segmentation and Classification of Diabetic Glomerulosclerosis. J. Am. Soc. Nephrol. 2019, 30, 1953–1967. [CrossRef] [PubMed]82.11 DOI: https://doi.org/10.1681/ASN.2018121259
Marsh, J.N.; Matlock, M.K.; Kudose, S.; Liu, T.C.; Stappenbeck, T.S.; Gaut, J.P.; Swamidass, S.J. Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans. Med. Imaging 2018, 37, 2718–2728. [CrossRef] [PubMed]83.12
Bukowy, J.D., et al., Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections. J Am Soc Nephrol, 2018. 29(8): p. 2081-2088102.42 DOI: https://doi.org/10.1681/ASN.2017111210
Kato, T., et al., Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image. BMC Bioinformatics, 2015. 16: p. 316102.44
Marsh, J.N., et al., Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections. IEEE Trans Med Imaging, 2018. 37(12): p. 2718-2728102.45 DOI: https://doi.org/10.1109/TMI.2018.2851150
Simon, O., et al., Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images. Sci Rep, 2018. 8(1): p. 2032103. DOI: https://doi.org/10.1038/s41598-018-20453-7
Kato T, Relator R, Ngouv H, Hirohashi Y, Takaki O, Kakimoto T, et al. Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image. BMC Bioinform. 2015;6:66108.20 DOI: https://doi.org/10.1186/s12859-015-0739-1
Kotyk T, Dey N, Ashour AS, Balas- Timar D, Chakraborty S, Ashour AS, et al. Measurement of glomerulus diameter and Bowman’s space width of renal albino rats. Comput Methods Prog Biomed. 2016;6:66109.22 DOI: https://doi.org/10.1016/j.cmpb.2015.10.023
Cascarano et al. BMC Med Inform Decis Mak (2021) 21:300
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Sudha Rani U, Dr. Subhas C
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
