Automated Multi-class Fetal Cardiac Vessel Segmentation in Aortic Arch Anomalies Using T2-Weighted 3D Fetal MRI

Paula Ramirez Gilliland*, Alena Uus, Milou P.M. van Poppel, Irina Grigorescu, Johannes K. Steinweg, David F.A. Lloyd, Kuberan Pushparajah, Andrew P. King, Maria Deprez

*Corresponding author for this work

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

Abstract

Congenital heart disease (CHD) encompasses a range of cardiac malformations present from birth, representing the leading congenital diagnosis. 3D volumetric reconstructions of T2w black blood fetal MRI provide optimal vessel visualisation, supporting prenatal CHD diagnosis, a key step for optimal patient management. We present a framework for automated multi-class fetal vessel segmentation in the setting where binary manual labels of the vessels region of interest (ROI) are available for training, as well as a multi-class labelled atlas. We combine deep learning label propagation from multi-class labelled condition-specific atlases with 3D Attention U-Net segmentation to achieve the desired multi-class output. We train a single network to segment 12 fetal cardiac vessels for three distinct aortic arch anomalies (double aortic arch, right aortic arch, and suspected coarctation of the aorta). Our segmentation network is trained by combination of a multi-class loss, which uses the propagated multi-class labels; and a binary loss which uses binary labels generated by expert clinicians. Our proposed method outperforms label propagation in accuracy of vessel segmentation, while succeeding in segmenting the anomaly area of all three CHD diagnoses included, achieving a 100% vessel detection rate.

Original languageEnglish
Title of host publicationPerinatal, Preterm and Paediatric Image Analysis - 7th International Workshop, PIPPI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsRoxane Licandro, Roxane Licandro, Andrew Melbourne, Jana Hutter, Esra Abaci Turk, Christopher Macgowan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages82-93
Number of pages12
ISBN (Print)9783031171161
DOIs
Publication statusPublished - 2022
Event7th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13575 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/202218/09/2022

Keywords

  • Atlas-based segmentation
  • Automated Segmentation
  • Congenital Heart Disease
  • Fetal Cardiac MRI
  • Label Propagation

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