Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network. Issue 2 (23rd March 2021)

• Record Type: Journal Article
• Authors: Daniel, Alexander J.; Buchanan, Charlotte E.; Allcock, Thomas; Scerri, Daniel; Cox, Eleanor F.; Prestwich, Benjamin L.; Francis, Susan T.
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 2(2021)
• Page Start: 1125
• Page End: 1136
• Publication Date: 2021-03-23
• Subjects: convolutional neural network -- kidney -- machine learning -- magnetic resonance imaging -- segmentation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28768
• Languages: English
• ISSNs: 0740-3194

Abstract

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.