Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Jeremy Tan*, Anselm Au, Qingjie Meng, Sandy FinesilverSmith, John Simpson, Daniel Rueckert, Reza Razavi, Thomas Day, David Lloyd, Bernhard Kainz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

8 Citations (Scopus)

Abstract

Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.

Original languageEnglish
Title of host publicationMedical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsYipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena
PublisherSpringer Science and Business Media Deutschland GmbH
Pages243-252
Number of pages10
ISBN (Print)9783030603335
DOIs
Publication statusPublished - 2020
Event1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12437 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/20208/10/2020

Keywords

  • Congenital heart disease
  • Fetal ultrasound

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