Cranial localization in 2D cranial ultrasound images using deep neural networks.

Premature neonates with intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) are at high risk for morbidity and mortality. Cranial ultrasound (CUS) is the most common imaging technique for early diagnosis of PHH during the first weeks after birth. Head size is one of the important indexes in the evaluation of PHH with CUS. In this paper, we present an automatic cranial localization method to help head size measurement in 2D CUS images acquired from premature neonates with IVH. We employ deep neural networks to localize the cranial region and minimum area bounding box. Separate deep neural networks are trained to detect the space parameters (position, scale, and orientation) of the bounding box. We evaluated the performance of the method on a set of 64 2D CUS images obtained from premature neonates with IVH through five-fold cross validation. Experimental results showed that the proposed method could estimate the cranial bounding box with the center point position error value of 0.33 +/- 0.32 mm, the orientation error value of 1.75 +/- 1.31 degrees, head height relative error (RE) value of 1.62 +/- 2.9 %, head width RE value of 1.22 +/- 1.24 %, head surface RE value of 2.27 +/- 3.04 %, average Dice similarity score of 0.97 +/- 0.01, and Hausdorff distance of 0.69 +/- 0.46 mm. The method is computationally efficient and has the potential to provide automatic head size measurement in the clinical evaluation of neonates.