Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Tan J, Au A, Meng Q, FinesilverSmith S, Simpson J, Rueckert D, Razavi R, Day T, Lloyd D, Kainz B (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12437 LNCS

Pages Range: 243-252

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lima, PER

ISBN: 9783030603335

DOI: 10.1007/978-3-030-60334-2_24

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.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Guy's and St Thomas' (NHS Foundation Trust) GB United Kingdom (GB)

How to cite

APA:

Tan, J., Au, A., Meng, Q., FinesilverSmith, S., Simpson, J., Rueckert, D.,... Kainz, B. (2020). Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening. In Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 243-252). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Tan, Jeremy, et al. "Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening." Proceedings of the 1st 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, PER Ed. Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena, Springer Science and Business Media Deutschland GmbH, 2020. 243-252.

BibTeX: Download