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A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.
- Source :
-
IEEE/ACM transactions on computational biology and bioinformatics [IEEE/ACM Trans Comput Biol Bioinform] 2024 Jul-Aug; Vol. 21 (4), pp. 1007-1018. Date of Electronic Publication: 2024 Aug 08. - Publication Year :
- 2024
-
Abstract
- Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and requires extensive anatomical knowledge and clinical experience. As such, efficiently and accurately detecting structures from real-world fetal ultrasound images is a key challenge. In this paper, we propose a YOLOX-based deep instance segmentation neural network (i.e., IS-YOLOX) for cardiac anatomical structure location and segmentation in fetal ultrasound images. Specifically, we reconstruct a new instance segmentation branch based on a multi-task deep learning framework. We then design a new multi-level non-maximum suppression (NMS) mechanism to further improve the segmentation performance that consists of three levels of selection. Moreover, unlike two-stage instance segmentation approaches, our method does not rely on object detection results. To the best of our knowledge, this is the first study regarding instance segmentation on 13 types of anatomical structures in the fetal four-chamber view. Extensive experiments were carried out on clinical datasets, and the experimental results show that our method outperforms nine competitive baselines.
- Subjects :
- Humans
Pregnancy
Female
Heart Defects, Congenital diagnostic imaging
Heart Defects, Congenital embryology
Echocardiography methods
Image Interpretation, Computer-Assisted methods
Algorithms
Ultrasonography, Prenatal methods
Neural Networks, Computer
Deep Learning
Fetal Heart diagnostic imaging
Fetal Heart anatomy & histology
Subjects
Details
- Language :
- English
- ISSN :
- 1557-9964
- Volume :
- 21
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- IEEE/ACM transactions on computational biology and bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 36378800
- Full Text :
- https://doi.org/10.1109/TCBB.2022.3222356