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. 2021 Aug;86(2):1125-1136.
doi: 10.1002/mrm.28768. Epub 2021 Mar 23.

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

Alexander J Daniel  1 , Charlotte E Buchanan  1 , Thomas Allcock  1 , Daniel Scerri  1 , Eleanor F Cox  1 , Benjamin L Prestwich  1 , Susan T Francis  1
Affiliations

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

Alexander J Daniel et al. Magn Reson Med. 2021 Aug.

Abstract

Purpose: Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T2 -weighted MRI to calculate TKV of healthy control (HC) and chronic kidney disease (CKD) patients.

Methods: This automated method uses machine learning, specifically a 2D convolutional neural network (CNN), to accurately segment the left and right kidneys from T2 -weighted MRI data. The data set consisted of 30 HC subjects and 30 CKD patients. The model was trained on 50 manually defined HC and CKD kidney segmentations. The model was subsequently evaluated on 50 test data sets, comprising data from 5 HCs and 5 CKD patients each scanned 5 times in a scan session to enable comparison of the precision of the CNN and manual segmentation of kidneys.

Results: The unseen test data processed by the 2D CNN had a mean Dice score of 0.93 ± 0.01. The difference between manual and automatically computed TKV was 1.2 ± 16.2 mL with a mean surface distance of 0.65 ± 0.21 mm. The variance in TKV measurements from repeat acquisitions on the same subject was significantly lower using the automated method compared to manual segmentation of the kidneys.

Conclusion: The 2D CNN method provides fully automated segmentation of the left and right kidney and calculation of TKV in <10 s on a standard office computer, allowing high data throughput and is a freely available executable.

Keywords: convolutional neural network; kidney; machine learning; magnetic resonance imaging; segmentation.

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References

REFERENCES

    1. Cox EF, Buchanan CE, Bradley CR, et al. Multiparametric renal magnetic resonance imaging: validation, interventions, and alterations in chronic kidney disease. Front Physiol. 2017;8:696.
    1. Cohen EI, Kelly SA, Edye M, Mitty HA, Bromberg JS. MRI estimation of total renal volume demonstrates significant association with healthy donor weight. Eur J Radiol. 2009;71:283-287.
    1. van den Dool SW, Wasser MN, de Fijter JW, Hoekstra J, van der Geest RJ. Functional renal volume: quantitative analysis at gadolinium-enhanced MR angiography-feasibility study in healthy potential kidney donors. Radiology. 2005;236:189-195.
    1. Chapman AB, Bost JE, Torres VE, et al. Kidney volume and functional outcomes in autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol. 2012;7:479-486.
    1. Tangri N, Hougen I, Alam A, Perrone R, McFarlane P, Pei Y. Total kidney volume as a biomarker of disease progression in autosomal dominant polycystic kidney disease. Can J Kidney Health Dis. 2017;4:2054358117693355.

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