Scanner independent deep learning-based segmentation framework applied to mouse embryos

Orlando Aristizabal, Daniel H. Turnbull, Jeffrey A. Ketterling, Yao Wang, Ziming Qiu, Tongda Xu, Hannah Goldman, Jonathan Mamou

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

Abstract

We have applied a deep learning framework, trained on mouse embryo images acquired with a 40 MHz annular array, to volumetric data acquired with a VisualSonics Vevo 3100 commercial scanner using a 40-MHz linear array. The deep learning framework was robust enough to accurately segment out the body and the brain ventricle from the 3D data generated by the commercial scanner. These results show that there is no need to retrain the algorithm with hundreds of new manually segmented datasets.

Original languageEnglish (US)
Title of host publicationIUS 2020 - International Ultrasonics Symposium, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781728154480
DOIs
StatePublished - Sep 7 2020
Event2020 IEEE International Ultrasonics Symposium, IUS 2020 - Las Vegas, United States
Duration: Sep 7 2020Sep 11 2020

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2020-September
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2020 IEEE International Ultrasonics Symposium, IUS 2020
CountryUnited States
CityLas Vegas
Period9/7/209/11/20

Keywords

  • Automatic segmentation
  • Convolutional network
  • Mouse embryo

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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