  Volumetric analysis of brain ventricle (BV) structure is a key tool in the
study of central nervous system development in embryonic mice. High-frequency
ultrasound (HFU) is the only non-invasive, real-time modality available for
rapid volumetric imaging of embryos in utero. However, manual segmentation of
the BV from HFU volumes is tedious, time-consuming, and requires specialized
expertise. In this paper, we propose a novel deep learning based BV
segmentation system for whole-body HFU images of mouse embryos. Our fully
automated system consists of two modules: localization and segmentation. It
first applies a volumetric convolutional neural network on a 3D sliding window
over the entire volume to identify a 3D bounding box containing the entire BV.
It then employs a fully convolutional network to segment the detected bounding
box into BV and background. The system achieves a Dice Similarity Coefficient
(DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set
surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of
25%.
