ArticleJournal of digital imaging2021
Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 35 citations in OpenAlex.
- Frugal Learning Methods for Kidney Segmentation in Non-Contrast MRI.Journal of clinical medicine · 2026Article
- A pilot study of magnetic resonance fingerprinting and radiomics analysis in autosomal dominant polycystic kidney disease.Kidney international · 2026Article
- Natural history of simple and complex cysts in autosomal dominant polycystic kidney disease on MRI.Communications medicine · 2026Article
- Characterization of Kidney and Liver Cystic Phenotype Associated with GANAB Using Advanced Imaging Biomarkers.Nephron · 2026Article
- In-depth 3D exploration of autosomal dominant polycystic kidney disease through light sheet fluorescence microscopy.Scientific reports · 2025Article
- Foam sclerotherapy for symptomatic cysts in ADPKD, ADPLD and solitary cysts.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Observational
- Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.Scientific reports · 2025Article
- Time-restricted eating and autosomal dominant polycystic kidney disease: a pilot, randomized clinical trial.Clinical kidney journal · 2025Article
- Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume.Biomedicines · 2025Article
- Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease.European radiology experimental · 2024Article
- Kidney Energetics and Cyst Burden in Autosomal Dominant Polycystic Kidney Disease: A Pilot Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024Article
- Deep learning-based liver cyst segmentation in MRI for autosomal dominant polycystic kidney disease.Radiology advances · 2024Article
- Deep Learning-Based Automated Imaging Classification of ADPKD.Kidney international reports · 2024Article
- AI models for automated segmentation of engineered polycystic kidney tubules.Scientific reports · 2024Article
- Total kidney volume in autosomal dominant polycystic kidney disease: intraobserver and interobserver agreement of two methods with MRI.Turkish journal of medical sciences · 2024Article
- Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study.Clinical kidney journal · 2023Article
- Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD.European radiology · 2023Article
- Abdominal Imaging in ADPKD: Beyond Total Kidney Volume.Journal of clinical medicine · 2023Review
- Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation.Kidney international · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Total kidney volume (TKV) is the main imaging biomarker used to monitor disease progression and to classify patients affected by autosomal dominant polycystic kidney disease (ADPKD) for clinical trials. However, patients with similar TKVs may have drastically different cystic presentations and phenotypes. In an effort to quantify these cystic differences, we developed the first 3D semantic instance cyst segmentation algorithm for kidneys in MR images. We have reformulated both the object detection/localization task and the instance-based segmentation task into a semantic segmentation task. This allowed us to solve this unique imaging problem efficiently, even for patients with thousands of cysts. To do this, a convolutional neural network (CNN) was trained to learn cyst edges and cyst cores. Images were converted from instance cyst segmentations to semantic edge-core segmentations by applying a 3D erosion morphology operator to up-sampled versions of the images. The reduced cysts were labeled as core; the eroded areas were dilated in 2D and labeled as edge. The network was trained on 30 MR images and validated on 10 MR images using a fourfold cross-validation procedure. The final ensemble model was tested on 20 MR images not seen during the initial training/validation. The results from the test set were compared to segmentations from two readers. The presented model achieved an averaged R
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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.