A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Shan Ling
  • Laifa Yan
  • Rongsong Mao
  • Jizhou Li
  • Haoran Xi
  • And 3 others
  • Fei Wang
  • Xiaolin Li
  • Min He

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4036-4047
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number7
Online published18 Apr 2024
Publication statusPublished - Jul 2024

Abstract

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions. In this study, we propose an automated method for segmenting the pulmonary artery, ascending aorta, and superior vena cava in the 3VV using a novel deep learning network named CoFi-Net. Our network incorporates a coarse-fine collaborative strategy with two parallel branches dedicated to simultaneous global localization and fine segmentation of the vessels. The coarse branch employs a partial decoder to leverage high-level semantic features, enabling global localization of objects and suppression of irrelevant structures. The fine branch utilizes attention-parameterized skip connections to improve feature representations and improve boundary information. The outputs of the two branches are fused to generate accurate vessel segmentations. Extensive experiments conducted on a collected dataset demonstrate the superiority of CoFi-Net compared to state-of-the-art segmentation models for 3VV segmentation, indicating its great potential for enhancing CHD diagnostic efficiency in clinical practice. Furthermore, CoFi-Net outperforms other deep learning models in breast lesion segmentation on a public breast ultrasound dataset, despite not being specifically designed for this task, demonstrating its potential and robustness for various segmentation tasks. © 2024 IEEE.

Research Area(s)

  • Breast, Congenital heart disease, Deep learning, Feature extraction, Fetal Echocardiography, Fetal heart, Image segmentation, Task analysis, Ultrasonic imaging, Ultrasound vessel segmentation

Citation Format(s)

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images. / Ling, Shan; Yan, Laifa; Mao, Rongsong et al.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 7, 07.2024, p. 4036-4047.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review