ArticleTomography (Ann Arbor, Mich.)2022
Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.
Article in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 30 citations in OpenAlex.
- Age, Sex, and Laterality-Based Evaluation of Renal Volume and Surface Area Using CT-Based Three-Dimensional Segmentation.Life (Basel, Switzerland) · 2026Article
- Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions.MedComm · 2026Review
- Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Article
- Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.Abdominal radiology (New York) · 2026Article
- Comment on: "3D holographic reconstruction and intraoperative navigation combined with CBL teaching in laparoscopic partial nephrectomy training for urology residents"-the hidden hurdle to scalability.Updates in surgery · 2026Article
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- 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
- Intergenerational Enlargement of Human Organs as a Driver of Increased Cancer Risk?BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- SwinDAF3D: Pyramid Swin Transformers with Deep Attentive Features for Automated Finger Joint Segmentation in 3D Ultrasound Images for Rheumatoid Arthritis Assessment.Bioengineering (Basel, Switzerland) · 2025Article
- Automated liver and spleen segmentation for MR elastography maps using U-Nets.Scientific reports · 2025Article
- Model-Assisted Spleen Contouring for Assessing Splenomegaly in Myelofibrosis: A Fast and Reproducible Approach to Evaluate Progression and Treatment Response.Journal of clinical medicine · 2025Article
- Multiscale and multimodal evaluation of autosomal dominant polycystic kidney disease development.Communications biology · 2024Article
- Deep learning-based liver cyst segmentation in MRI for autosomal dominant polycystic kidney disease.Radiology advances · 2024Article
- Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.Scientific reports · 2024Article
- Deep learning-based automatic measurement system for patellar height: a multicenter retrospective study.Journal of orthopaedic surgery and research · 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
- The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.Scientific reports · 2023Article
- Size Matters: How to Characterize ADPKD Severity by Measuring Total Kidney Volume.Journal of clinical medicine · 2023Article
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.BMC bioinformatics · 2023Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.