Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
This addresses a domain-specific problem for clinicians in fetal ultrasound by automating a manual task, though it is incremental as it builds on existing neural network methods.
The paper tackles the problem of automatically detecting standard scan planes in 3D fetal ultrasound volumes, which is labor-intensive and requires expert knowledge, by proposing an Iterative Transformation Network (ITN) that achieves errors of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for two key planes and takes 0.46 seconds per plane.
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for the transventricular and transcerebellar planes respectively and takes 0.46s per plane. Source code is publicly available at https://github.com/yuanwei1989/plane-detection.