Automatic body localization and brain ventricle segmentation in 3D high frequency ultrasound images of mouse embryos

Abstract

This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.

Deep Bv: A Fully Automated System for Brain Ventricle Localization and Segmentation In 3D Ultrasound Images of Embryonic Mice

“…The main contribution of this work is the successful development of a fully automatic framework using deep learning that achieves state-of-the-art BV segmentation results. Compared to the previous state-of-the-art, a traditional graphical model based method [4], our proposed deep learning based framework is much more robust to variation in embryo body orientation, BV shape and BV location. It can obtain satisfactory results even when the image has highly inconsistent intensity distributions and severe missing boundaries.…”

“…A previous work [4] introduced a dataset of 36 HFU volumes with manual BV segmentations. In this paper, 370 additional whole-body image volumes with manual BV segmentations are used.…”

“…The mean DSC for the segmented BV by the NGC based framework[4] and our proposed method on the unseen 111 HFU volume test set.…”

Deep Mouse: An End-to-End Auto-Context Refinement Framework for Brain Ventricle & Body Segmentation in Embryonic Mice Ultrasound Volumes

“…Inspired by the success of fully convolutional networks (FCN) for semantic segmentation [6], a deep-learning based framework for BV segmentation was proposed in [7] which outperformed the NGC based framework in [5] by a large margin. Because the BV makes up a very small portion (<0.5%) of the whole volume, the algorithm [7] first applied a volumetric convolutional neural network (CNN) on a 3D sliding window over the entire volume to identify a 3D bounding box containing the entire BV, followed by a FCN to segment the detected bounding box into BV or background.…”

“…Earlier works attempted to address segmentation of volumetric embryonic data. Nested Graph Cut (NGC) [4] was developed to perform the segmentation of the BV from a HFU mouse embryo head image manually cropped from a wholebody scan, and [5] extended to perform BV segmentation in whole-body images, which worked well on a small data set but failed to generalize to a larger unseen data set.…”