Comparative Analysis of Kidney Histomorphometry Utilizing Two Distinct Image Processing Software

Ageng Brahmadhi, Ira Citra Ningrom


DOI: https://doi.org/10.14710/jbtr.v9i3.18554

Abstract


ABSTRACT

Background: Histopathological examination is critical to evaluate tissue condition. An accurate assessment is necessary for diagnosis establishment. Nowadays, both quantitative and qualitative scoring are enhanced with computer-assisted image analysis to reduce bias. Various software was developed to assist in image analysis. The question of whether the measurement results from one software will be comparable to those from another software may come up, given the wide variety of software options. Nevertheless, this subject is only occasionally discussed.

Objective: This study aimed to compare the measurement results from Fiji and QuPath software in kidney histomorphometry.

Methods: Normal kidney histological slide was observed. Selected histological structures, including the renal corpuscle area, glomerular area, Bowman space area, inner diameter of proximal, distal, and Henle loop, were measured using QuPath and Fiji software. The measurement results from the two software were compared for value differences and agreement analysis.

Results: The renal corpuscle means the area was 12.7x103 µm2 in QuPath and 12.5 x103 µm2 in Fiji. The glomerular area was 7.8 x103 µm2 for both software. The proximal tubule's inner diameters varied from 18.7 to 150.8 µm. Smaller inner diameters were observed in distal tubules (17.1-80.5 µm) and The Henle loop (15.5-69.6 µm). There was no significant difference in measurement results of particular structures between the compared software (P-value > 0.05). The further confirmational analysis supported the similarity between the two measurement results.

Conclusion: the measurement result of kidney microstructures using QuPath and Fiji were identical.


Keywords


Kidney; Histology; Computer-assisted; Histomorphometry

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References


Klopfleisch R. Multiparametric and semiquantitative scoring systems for the evaluation of mouse model histopathology--a systematic review. BMC Vet Res. 2013;9:123. doi: 10.1186/1746-6148-9-123.

Meyerholz DK, Beck AP. Fundamental Concepts for Semiquantitative Tissue Scoring in Translational Research. ILAR J. 2018;59(1):13-7. doi: 10.1093/ilar/ily025.

Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics. 2017;18(1):1-26. doi: 10.1186/s12859-017-1934-z.

Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-82. doi: 10.1038/nmeth.2019.

Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics. 2008;9(1):482. doi: 10.1186/1471-2105-9-482.

Piccinini F, Balassa T, Szkalisity A, Molnar C, Paavolainen L, Kujala K, et al. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data. Cell Syst. 2017;4(6):651-5. doi: 10.1016/j.cels.2017.05.012.

Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16(12):1226-32. doi: 10.1038/s41592-019-0582-9.

Sommer C, Hoefler R, Samwer M, Gerlich DW. A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol Biol Cell. 2017;28(23):3428-36. doi: 10.1091/mbc.E17-05-0333.

Farahani N, Parwani A, Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int. 2015;7:23-33. doi: 10.2147/PLMI.S59826.

Kumar N, Gupta R, Gupta S. Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions. J Digit Imaging. 2020;33(4):1034-40. doi: 10.1007/s10278-020-00351-z.

Rangan GK, Tesch GH. Quantification of renal pathology by image analysis (Methods in Renal Research). Nephrology. 2007;12(6):553-8. doi: 10.1111/j.1440-1797.2007.00855.x.

Cho K-O, Lee SH, Jang H-J. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2019;24(1):89-99. doi: 10.4196/kjpp.2020.24.1.89.

Samuel T, Hoy WE, Douglas-Denton R, Hughson MD, Bertram JF. Applicability of the glomerular size distribution coefficient in assessing human glomerular volume: the Weibel and Gomez method revisited. J Anat. 2007;210(5):578-82. doi: 10.1111/j.1469-7580.2007.00715.x.

Sakineh A, Armin A. Renal Biopsy Interpretation. In: Muhammed M, Javed IK, editors. Topics in Renal Biopsy and Pathology. Rijeka: IntechOpen; 2012. p. 45-64. doi: 10.5772/26316.

Turgut D. Measurement of glomerular area in primary glomerular diseases with a digital pathology software. Journal of clinical and experimental investigations. 2021;48(1):9-16. doi: 10.5798/dicletip.887368.

Zamami R, Kohagura K, Kinjyo K, Nakamura T, Kinjo T, Yamazato M, et al. The Association between Glomerular Diameter and Secondary Focal Segmental Glomerulosclerosis in Chronic Kidney Disease. Kidney Blood Press Res. 2021;46(4):433-40. doi: 10.1159/000515528.

Morozov D, Parvin N, Charlton JR, Bennett KM. Mapping kidney tubule diameter ex vivo by diffusion MRI. Am J Physiol Renal Physiol. 2021;320(5):F934-F46. doi: 10.1152/ajprenal.00369.2020.

Li Q, Onozato ML, Andrews PM, Chen CW, Paek A, Naphas R, et al. Automated quantification of microstructural dimensions of the human kidney using optical coherence tomography (OCT). Opt Express. 2009;17(18):16000-16. doi: 10.1364/oe.17.016000.

Barrett KE, Barman SM, Yuan JXJ, Brooks H. Ganong's review of medical physiology. 26th ed. ed. New York, N.Y: McGraw-Hill Education LLC.; 2019.

Ortega-Martinez M, Gutierrez-Davila V, Gutierrez-Arenas E, Niderhauser-Garcia A, Cerda-Flores RM, Jaramillo-Rangel G. The Convoluted Tubules of the Nephron Must Be Considered Elliptical, and Not Circular, in Stereological Studies of the Kidney. Kidney Blood Press Res. 2021;46(2):229-35. doi: 10.1159/000515051.

Alicelebić S. Proximal convoluted tubules of the rats kidney--a stereological analysis. Bosn J Basic Med Sci. 2003;3(1):36-9. doi: 10.17305/bjbms.2003.3568.




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