Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning. In: Cardoso MJ, Arbel T, Melbourne A, Bogunovic H, Moeskops P, Chen X, Schwartz E, Garvin M, Robinson E, Trucco E, Ebner M, Xu Y, Makropoulos A, Desjardin A, Vercauteren T, editors. Fetal, Infant and Ophthalmic Medical Image Analysis. Cham: Springer International Publishing; 2017. pp. 62-72. [DOI: 10.1007/978-3-319-67561-9_7]

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

Automated 3D ultrasound image analysis for first trimester assessment of fetal health

The first trimester fetal ultrasound scan is important to confirm fetal viability, to estimate the gestational age of the fetus, and to detect fetal anomalies early in pregnancy. First trimester ultrasound images have a different appearance than for the second trimester scan, reflecting the different stage of fetal development. There is limited literature on automation of image-based assessment for this earlier trimester, and most of the literature is focused on one specific fetal anatomy. In this paper, we consider automation to support first trimester fetal assessment of multiple fetal anatomies including both visualization and the measurements from a single 3D ultrasound scan. We present a deep learning and image processing solution (i) to perform semantic segmentation of the whole fetus, (ii) to estimate plane orientation for standard biometry views, (iii) to localize and automatically estimate biometry, and (iv) to detect fetal limbs from a 3D first trimester volume. Computational analysis methods were built using a real-world dataset (n  =  44 volumes). An evaluation on a further independent clinical dataset (n  =  21 volumes) showed that the automated methods approached human expert assessment of a 3D volume.