Dilated Squeeze-and-Excitation U-Net for Fetal Ultrasound Image Segmentation
Donghao Qiao, Farhana Zulkernine
Abstract
During all trimesters of the pregnancy, measuring the fetal Head Circumference (HC) from ultrasound images can estimate the gestational age, monitor the growth status of the fetus and infer newborn's state. Precise segmentation of fetal ultrasound images can help physicians measure HC efficiently and accurately and make further predictions. In this paper, we leverage deep learning encode-decode architecture to segment the fetal skull boundary and fetal skull for fetal HC measurement. We modify our network based on U-Net due to its outstanding performance in biomedical image analysis. We add dilated convolution layers after the last encoder and Squeeze-and-Excitation (SE) blocks on the skip connections of U-Net to segment fetal skull boundary and fetal skull in 2D ultrasound images. The model is trained and evaluated on the HC18 grand challenge dataset, which has 2D ultrasound images at different trimesters of pregnancy. We achieved 2.27 ± 3.61 mm mean absolute difference in HC measurement. The model also achieved 97.31 ± 1.84% mean Dice score in fetal skull segmentation.