ArticleAbdominal radiology (New York)2022
Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 34 citations in OpenAlex.
- A z-score-based reference system for renal volume using three-dimensional ultrasound: from model to clinical tool.Quantitative imaging in medicine and surgery · 2026Article
- Age, Sex, and Laterality-Based Evaluation of Renal Volume and Surface Area Using CT-Based Three-Dimensional Segmentation.Life (Basel, Switzerland) · 2026Article
- Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation.Journal of imaging informatics in medicine · 2026Article
- Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Article
- Are Image-Based Deep Learning Algorithms of Kidney Volume in Polycystic Kidney Disease Ready for Clinical Deployment? A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Comparison Between the Human-Sourced Ellipsoid Method and Kidney Volumetry Using Artificial Intelligence in Polycystic Kidney Disease.Journal of personalized medicine · 2025Article
- TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research.Scientific data · 2025Article
- Ultrasound versus magnetic resonance imaging for calculating total kidney volume in patients with ADPKD: a real-world data analysis.The ultrasound journal · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.Academic radiology · 2024Article
- Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.BMC medical imaging · 2023Article
- Abdominal Imaging in ADPKD: Beyond Total Kidney Volume.Journal of clinical medicine · 2023Review
- Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease.Investigative and clinical urology · 2023Article
- Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.Tomography (Ann Arbor, Mich.) · 2022Article
- Update on the Application of Ultrasonography in Understanding Autosomal Dominant Polycystic Kidney Disease.Journal of medical ultrasoundReview
- Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence.Indian journal of nephrologyReview
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeTotal kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients.
methodWe used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison.
resultsOur method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R
conclusionThis is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.
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