Automatic mouse embryo brain ventricle segmentation, gestation stage estimation, and mutant detection from 3D 40-MHz ultrasound data

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Abstract
Volumetric analysis of brain ventricles (BVs) is important to the study of normal and abnormal development of the central nervous system of mouse embryos. High-frequency ultrasound (HFU) is frequently used to image embryos because it is real-time, non-invasive, and provides fine-resolution images. However, manual segmentation of BVs from 3D HFU volumes remains challenging and time consuming. Therefore, automatic segmentation, staging, and mutant detection algorithms are needed for studies with large embryo counts. An accurate and automatic method to segment BVs from high-frequency ultrasound images has been stated in a prior work. This paper presents novel algorithms for deriving the Y-skeleton of a BV region and decomposing the BV region into five components (fourth ventricle, aqueduct, third ventricle and two lateral ventricles). Embryo staging and mutant detection are accomplished by analyzing the volume profile along the BV skeleton and the volumes of the five BV components.