Deep Learning-based Spine Centerline Extraction in Fetal Ultrasound

Astrid Franz, Alexander Schmidt-Richberg, Eliza Orasanu, Cristian Lorenz

Abstract: Ultrasound is widely used for fetal screening. It allows for detecting abnormalities at an early gestational age, while being time and cost effective with no known adverse effects. Searching for optimal ultrasound planes for these investigations is a demanding and time-consuming task. Here we describe a method for automatically detecting the spine centerline in 3D fetal ultrasound images. We propose a two-stage approach combining deep learning and classic image processing techniques. First, we segment the spine using a deep learning approach. The resulting probability map is used as input for a tracing algorithm. The result is a sequence of points describing the spine centerline. This line can be used for measuring the spinal length and for generating view planes for the investigation of anomalies.