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

Ziming Qiu, Nitin Nair, Jack Langerman, Orlando Aristizabal, Jonathan Mamou, Daniel H. Turnbull, Jeffrey A. Ketterling, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2019 IEEE International Ultrasonics Symposium, IUS 2019
PublisherIEEE Computer Society
Pages12-15
Number of pages4
ISBN (Electronic)9781728145969
DOIs
StatePublished - Oct 2019
Event2019 IEEE International Ultrasonics Symposium, IUS 2019 - Glasgow, United Kingdom
Duration: Oct 6 2019Oct 9 2019

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2019-October
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2019 IEEE International Ultrasonics Symposium, IUS 2019
CountryUnited Kingdom
CityGlasgow
Period10/6/1910/9/19

Keywords

  • deep learning
  • explainable ai
  • mutant classification
  • segmentation
  • ultrasound
  • visualization

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

Fingerprint Dive into the research topics of 'Automatic Mouse Embryo Brain Ventricle & Body Segmentation and Mutant Classification from Ultrasound Data Using Deep Learning'. Together they form a unique fingerprint.

  • Cite this

    Qiu, Z., Nair, N., Langerman, J., Aristizabal, O., Mamou, J., Turnbull, D. H., Ketterling, J. A., & Wang, Y. (2019). Automatic Mouse Embryo Brain Ventricle & Body Segmentation and Mutant Classification from Ultrasound Data Using Deep Learning. In 2019 IEEE International Ultrasonics Symposium, IUS 2019 (pp. 12-15). [8925720] (IEEE International Ultrasonics Symposium, IUS; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/ULTSYM.2019.8925720