Learning-based scan plane identification from fetal head ultrasound images

Abstract

Acquisition of a clinically acceptable scan plane is a pre-requisite for ultrasonic measurement of anatomical features from B-mode images. In obstetric ultrasound, measurement of gestational age predictors, such as biparietal diameter and head circumference, is performed at the level of the thalami and cavum septum pelucidi. In an accurate scan plane, the head can be modeled as an ellipse, the thalami looks like a butterfly, the cavum appears like an empty box and the falx is a straight line along the major axis of a symmetric ellipse inclined either parallel to or at small angles to the probe surface. Arriving at the correct probe placement on the mother's belly to obtain an accurate scan plane is a task of considerable challenge especially for a new user of ultrasound. In this work, we present a novel automated learning-based algorithm to identify an acceptable fetal head scan plane. We divide the problem into cranium detection and a template matching to capture the composite "butterfly" structure present inside the head, which mimics the visual cues used by an expert. The algorithm uses the stateof- the-art Active Appearance Models techniques from the image processing and computer vision literature and tie them to presence or absence of the inclusions within the head to automatically compute a score to represent the goodness of a scan plane. This automated technique can be potentially used to train and aid new users of ultrasound.