ArticleScientific reports2019
Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
- Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in Versa HD Linear Accelerator.Advanced biomedical research · 2026Article
- CT-free kidney single-photon emission computed tomography for glomerular filtration rate.Scientific reports · 2025Article
- A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging.Scientific reports · 2025Article
- Semiautomatic volume measure of kidney vascular territories on CT angiography to plan aortic aneurysm repair in patients with horseshoe kidney.European radiology experimental · 2024Article
- A comprehensive review on Compton camera image reconstruction: from principles to AI innovations.Biomedical engineering letters · 2024Review
- Article
- Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.EJNMMI physics · 2024Article
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Multi-modal co-learning with attention mechanism for head and neck tumor segmentation onEJNMMI physics · 2024Article
- Deep learning-based measurement of split glomerular filtration rate withEJNMMI physics · 2024Article
- Single photon emission computed tomography/computed tomography imaging of gouty arthritis: A new voice.Journal of translational internal medicine · 2024Article
- Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.Nuclear medicine and molecular imaging · 2024Article
- Correlation between bilateral GFR in patients with localized renal cancer after partial nephrectomy.International urology and nephrology · 2024Article
- A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.Annals of nuclear medicine · 2024Article
- UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning.World journal of urology · 2024Article
- Deep learning-based material decomposition of iodine and calcium in mobile photon counting detector CT.PloS one · 2024Article
- Article
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Automatic Lung Cancer Segmentation in [Nuclear medicine and molecular imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative SPECT/CT is potentially useful for more accurate and reliable measurement of glomerular filtration rate (GFR) than conventional planar scintigraphy. However, manual drawing of a volume of interest (VOI) on renal parenchyma in CT images is a labor-intensive and time-consuming task. The aim of this study is to develop a fully automated GFR quantification method based on a deep learning approach to the 3D segmentation of kidney parenchyma in CT. We automatically segmented the kidneys in CT images using the proposed method with remarkably high Dice similarity coefficient relative to the manual segmentation (mean = 0.89). The GFR values derived using manual and automatic segmentation methods were strongly correlated (R2 = 0.96). The absolute difference between the individual GFR values using manual and automatic methods was only 2.90%. Moreover, the two segmentation methods had comparable performance in the urolithiasis patients and kidney donors. Furthermore, both segmentation modalities showed significantly decreased individual GFR in symptomatic kidneys compared with the normal or asymptomatic kidney groups. The proposed approach enables fast and accurate GFR measurement.
Indexed as
Identifiers
What OpenQuestion holds
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.