Automatic measurement of fetal lung volume by 3D ultrasound based on 3D-nnUnet

Abstract

Fetal lung dysplasia often causes the severe respiratory distress and even neonatal death at birth. Fetal lung volume measurement is an important method for clinical non-invasive assessment of lung maturity. However, the existing methods for measuring fetal lung volume suffer from huge errors, tedious and time-consuming operations, and poor clinical practicability. For this reason, we first propose an efficient and stable automatic segmentation and measurement method for fetal lung volume based on 3D-nnUnet, which uses the network adaptation to effectively overcome the problems of low tissue contrast and blurred edges and achieves accurate segmentation of 3-dimensional (3D) fetal lung ultrasound images. In addition, to solve the problem of large difference and extremely uneven distribution of sample numbers at different gestational weeks, we innovatively propose a hard-mining and easy-penalized (HMEP) loss to improve the model generalization ability. Compared with the segmentation results of 2-dimension optimal DeepLab V3+ and 3D-Unet, our network model has the best segmentation performance, and the accuracy is as high as 85.7%. In addition, HMEP loss enables the 3D-nnUnet model to focus on learning a few difficult samples, which increases the segmentation accuracy by nearly 2%. There is no significant statistical difference between the fetal lung volumes measured by our network and gold standard using the consistency test in different gestational weeks. The experimental results show that our method can efficiently realize the automatic and accurate segmentation and volume measurement of 3D ultrasound fetal lung, and has the good stability and generalization ability, which can avoid the problems of cumbersome, time-consuming operations and large errors in the previous fetal lung volume measurement methods. Our method has the promising application prospects in diagnosing fetal lung dysplasia and assessing fetal lung maturity.