Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidne
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KIDNEYS, URETERS, BLADDER, RETROPERITONEUM
Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal‑dominant polycystic kidney disease Timothy L. Kline1,2 · Marie E. Edwards2 · Jeffrey Fetzer1 · Adriana V. Gregory2 · Deema Anaam1 · Andrew J. Metzger2 · Bradley J. Erickson1 Received: 30 April 2020 / Revised: 26 August 2020 / Accepted: 3 September 2020 © The Author(s) 2020
Abstract Purpose For patients affected by autosomal-dominant polycystic kidney disease (ADPKD), successful differentiation of cysts is useful for automatic classification of patient phenotypes, clinical decision-making, and disease progression. The objective was to develop and evaluate a fully automated semantic segmentation method to differentiate and analyze renal cysts in patients with ADPKD. Methods An automated deep learning approach using a convolutional neural network was trained, validated, and tested on a set of 60 MR T2-weighted images. A three-fold cross-validation approach was used to train three models on distinct training and validation sets (n = 40). An ensemble model was then built and tested on the hold out cases (n = 20), with each of the cases compared to manual segmentations performed by two readers. Segmentation agreement between readers and the automated method was assessed. Results The automated approach was found to perform at the level of interobserver variability. The automated approach had a Dice coefficient (mean ± standard deviation) of 0.86 ± 0.10 vs Reader-1 and 0.84 ± 0.11 vs. Reader-2. Interobserver Dice was 0.86 ± 0.08. In terms of total cyst volume (TCV), the automated approach had a percent difference of 3.9 ± 19.1% vs Reader-1 and 8.0 ± 24.1% vs Reader-2, whereas interobserver variability was − 2.0 ± 16.4%. Conclusion This study developed and validated a fully automated approach for performing semantic segmentation of kidney cysts in MR images of patients affected by ADPKD. This approach will be useful for exploring additional imaging biomarkers of ADPKD and automatically classifying phenotypes. Keywords Autosomal-dominant polycystic kidney disease · Semantic cyst segmentation · Deep learning · Magnetic resonance imaging
Introduction Autosomal-dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease, affecting roughly 12 million people worldwide, and is currently the fourth leading cause of kidney failure [1, 2]. Its pathology is such that the continuous growth of cysts causes a progressive increase in total kidney volume (TKV). A typical ADPKD patient exhibits progressive renal function decline and * Timothy L. Kline [email protected] 1
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
2
roughly 70% progress to end-stage renal disease between age 40 and age 70 [3, 4]. TKV has been shown in a number of studies to be a useful predictor of ADPKD progression [5–7]. Similarly, the ability to delineate and measure cystic burden further
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