Fetal gestational age estimation without clinical measurement using deep learning

Gestational age estimation is a key marker in obstetric care for determination of fetal growth and health. Current clinical estimation of gestational age is based on regression using clinical fetal growth charts and biometry measurements by an experienced sonographer. However, estimation of gestational age using biometry is difficult especially in late pregnancy, and there may be additional salient anatomical information in images to assist gestational age estimation in standard planes which is discarded by clinical biometry measurement. This thesis therefore attempts to use deep neural network to directly regress gestational age from fetal standard planes without pixel size information, thus relying on anatomical appearance alone without fetal biometry. In this thesis, we first investigate the data augmentation methods commonly used for medical image analysis and implement an data augmentation selection strategy. We find that this optimized augmentation strategy leads to increased performance on the proxy task of standard plane classification compared to conventional hand-crafted data augmentation methods. We then use this data augmentation strategy selection framework to train a deep convolutional neural network for gestational age estimation from the head, abdomen and femur standard planes, and investigate the performance of different loss functions for deep regression. We demonstrate gestational age estimation across the second and third trimester on the head standard plane with a mean absolute error of 0.6 weeks with the optimized data augmentation policy, and use the trained neural network to visualize anatomically salient areas of the input image. We then extend the deep learning framework by introducing priors to trainable weights, making a Bayesian neural network. The Bayesian neural network separately estimates aleatoric and epistemic uncertainty during gestational age inference. This allows for calibrated uncertainties during gestational age estimation. This is the first work to estimate fetal gestational age from ultrasound standard plane images without pixel size or biometry information, and may be potentially of use in low- and medium- income countries where accurate gestational age dating in late pregnancy is important as women present to obstetric care in all stages of pregnancy.