Deep Learning Based Uterus Localization and Anatomical Structure Segmentation on Fetal Ultrasound Image

Abstract

This paper proposes deep learning based methods for automatically detecting the uterus in the ultrasound (US) image and segmenting the detected uterus into anatomical structures. For accurate detection of the uterus and for segmentation of multiple fine-grained anatomical structures from the US image, we use a two-tier deep learning based algorithm: (I) localizing the bounding box of the uterus, and (II) segmenting the areas of amniotic fluid and fetal body from uterine image. To achieve (I) we design and train a convolutional neural network (CNN) based bounding box regression model, which regresses candidate positions of the uterus. Then we use the cropped uterus region as the input to a semantic segmentation approach. For (II) we apply fully convolution based architecture that segments the fetal body and amniotic fluid from fetal US images in an end-to-end, supervised learning pipeline. We use additional inner layers and intermediate supervisions to improve the segmentation accuracy and smooth out the boundaries. We experimentally evaluate our methods and demonstrate the accurate uterus detection and anatomical structure segmentation results.