In pregnancy care, it is useful to identify pregnancies that have an increased risk of complications as early as possible. In this way, these women can be followed more closely, and preventive measures can be taken if necessary. Today, at least one ultrasound examination of the fetus is typically performed in the course of the pregnancy. Here, several features of the pregnancy may be evaluated, such as the number of fetuses, the location of the placenta and the fetus’ development and anatomy.
Over the last 10–15 years, it has also been established an association between the volume of the placenta and the risk of complications. However, in order to measure this volume during the pregnancy, one must have good three-dimensional (3D) images. Ultrasound probes for 3D imaging are becoming more common, but these cover a limited area, and it is often not possible to fit the entire placenta within one image.
In this project, we will develop a method for measuring the placental volume automatically by stitching together two-dimensional (2D) images from a normal ultrasound probe. This we will do by equipping the probe with a position sensor, and then image a large number of placentas while measuring the position of each individual ultrasound image. Using modern machine learning methods we can then teach a computer program how 2D images can be combined into a 3D image based solely on what is seen in the images. These 3D images can then be used to calculate the volume of the placenta.
In the long term, the method we are developing can be used on any ultrasound scanner, and it can therefore help identify many high-risk pregnancies that go unnoticed today. This is necessary to be able to take measures that could potentially prevent damage to the fetus or, in the worst case, fetal death. The method can thus improve maternal care all over the world.