Paper Info
Title
Automatic Segmentation of Neonatal Ventricles from Cranial Ultrasound for Prediction of Intraventricular Hemorrhage Outcome.
Abstract
Intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) in premature neonates is one of the recognized reasons of brain injury in newborns. Cranial ultrasound (CUS) is a noninvasive imaging tool that has been used widely to diagnose and monitor neonates with IVH. In our previous work, we showed the potential of quantitative morphological analysis of lateral ventricles from early CUS to predict the PHH outcome in neonates with IVH. In this paper, we first present a new automatic method for ventricle segmentation in 2D CUS images. We detect the brain bounding box and brain mid-line to estimate the anatomical positions of ventricles and correct the brain rotation. The ventricles are segmented using a combination of fuzzy c-means, phase congruency, and active contour algorithms. Finally, we compare this fully automated approach with our previous work for the prediction of the outcome of PHH on a set of 2D CUS images taken from 60 premature neonates with different IVH grades. Experimental results showed that our method could segment ventricles with an average Dice similarity coefficient of 0.8 ± 0.12. In addition, our fully automated method could predict the outcome of PHH based on the extracted ventricle regions with similar accuracy to our previous semi-automated approach (83% vs. 84%, respectively, p-value = 0.8). This method has the potential to standardize the evaluation of CUS images and can be a helpful clinical tool for early monitoring and treatment of IVH and PHH.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8513097
EMBC
Field
DocType
Volume
Lateral ventricles,Computer vision,Segmentation,Computer science,Image segmentation,Intraventricular hemorrhage,Cranial ultrasound,Artificial intelligence,Ventricle,Radiology,Noninvasive imaging,Post-Hemorrhagic Hydrocephalus
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Pooneh. R. Tabrizi182.95
Rawad Obeid211.03
Juan J Cerrolaza311517.01
Anna Penn400.68
Awais Mansoor56812.49
Marius George Linguraru636248.94