  High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due
to its noninvasive and real-time characteristics. However, manual segmentation
of the brain ventricles (BVs) and body requires substantial time and expertise.
This work proposes a novel deep learning based end-to-end auto-context
refinement framework, consisting of two stages. The first stage produces a low
resolution segmentation of the BV and body simultaneously. The resulting
probability map for each object (BV or body) is then used to crop a region of
interest (ROI) around the target object in both the original image and the
probability map to provide context to the refinement segmentation network.
Joint training of the two stages provides significant improvement in Dice
Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906
for the BV, and 0.919 to 0.934 for the body). The proposed method significantly
reduces the inference time (102.36 to 0.09 s/volume around 1000x faster) while
slightly improves the segmentation accuracy over the previous methods using
slide-window approaches.
