Automatic Mouse Embryo Brain Ventricle & Body Segmentation and Mutant Classification From Ultrasound Data Using Deep Learning

TL;DR

This paper introduces a deep learning pipeline for automated segmentation of mouse embryo brain ventricles and body in 3D ultrasound images, enabling efficient mutation classification with high accuracy.

Contribution

It presents a novel deep learning approach for automated segmentation and mutation detection in embryonic mouse ultrasound data, addressing challenges of shape variation and artifacts.

Findings

Achieved 0.896 DSC for ventricle segmentation

Attained 0.925 DSC for body segmentation

Reached 95.8% accuracy in mutant classification

Abstract

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice in vivo because it is non-invasive and real-time. Manual segmentation of the brain ventricles (BVs) and whole body from 3D HFU images is time-consuming and requires specialized training. This paper presents a deep-learning-based segmentation pipeline which automates several time-consuming, repetitive tasks currently performed to study genetic mutations in developing mouse embryos. Namely, the pipeline accurately segments the BV and body regions in 3D HFU images of mouse embryos, despite significant challenges due to position and shape variation of the embryos, as well as imaging artifacts. Based on the BV segmentation, a 3D convolutional neural network (CNN) is further trained to detect embryos with the Engrailed-1 (En1) mutation. The algorithms achieve 0.896 and 0.925 Dice Similarity Coefficient (DSC) for BV and body segmentation, respectively, and 95.8% accuracy on mutant classification. Through gradient based interrogation and visualization of the trained classifier, it is demonstrated that the model focuses on the morphological structures known to be affected by the En1 mutation.