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Automatic Segmentation of The Renal Collecting System in 3D Pediatric Ultrasound to Assess the Severity of Hydronephrosis.

, , , , , , and . ISBI, page 1717-1720. IEEE, (2019)

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Automatic Segmentation of The Renal Collecting System in 3D Pediatric Ultrasound to Assess the Severity of Hydronephrosis., , , , , , and . ISBI, page 1717-1720. IEEE, (2019)Ultrasound-Based Phenotyping of Lateral Ventricles to Predict Hydrocephalus Outcome in Premature Neonates., , , , , , , and . IEEE Trans. Biomed. Eng., 67 (11): 3026-3034 (2020)Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105751Z. SPIE, (2018)Shape-based acetabular cartilage segmentation: application to CT and MRI datasets., , , , and . Int. J. Comput. Assist. Radiol. Surg., 11 (7): 1247-1265 (2016)Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 10 (4): 433-446 (2015)Cranial ultrasound-based prediction of post hemorrhagic hydrocephalus outcome in premature neonates with intraventricular hemorrhage., , , , , , and . EMBC, page 169-172. IEEE, (2017)Automatic kidney segmentation in 3D pediatric ultrasound images using deep neural networks and weighted fuzzy active shape model., , , , and . ISBI, page 1170-1173. IEEE, (2018)Cranial localization in 2D cranial ultrasound images using deep neural networks., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 1095026. SPIE, (2019)Automatic Segmentation of Neonatal Ventricles from Cranial Ultrasound for Prediction of Intraventricular Hemorrhage Outcome., , , , , and . EMBC, page 3136-3139. IEEE, (2018)Pediatric Hydronephrosis Severity Assessment Using Convolutional Neural Networks With Standardized Ultrasound Images., , , , , , , , and . ISBI, page 1803-1806. IEEE, (2021)