Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements.
Jaidip M JagtapAdriana V GregoryHeather L HomesDarryl E WrightMarie E EdwardsZeynettin AkkusBradley J EricksonTimothy L KlinePublished in: Abdominal radiology (New York) (2022)
This 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.
Keyphrases
- deep learning
- polycystic kidney disease
- convolutional neural network
- end stage renal disease
- high resolution
- magnetic resonance imaging
- artificial intelligence
- contrast enhanced
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- endothelial cells
- peritoneal dialysis
- prognostic factors
- magnetic resonance
- computed tomography
- cross sectional
- optical coherence tomography
- patient reported outcomes
- diffusion weighted imaging
- fluorescence imaging