Hybrid method for automatic initialization and segmentation of ventricular on large-scale cardiovascular magnetic resonance images

• Published in: BMC medical imaging Vol. 25; no. 1; pp. 155 - 13
• Main Authors: Pan, Ning, Li, Zhi, Xu, Cailu, Gao, Junfeng, Hu, Huaifei
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.05.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Cardiac function; Cardiac image analysis; Cardiovascular diseases; Complex transformation; Contours; Deep Learning; Diagnosis; Epicardium; Ground truth; Heart Ventricles - diagnostic imaging; Humans; Image acquisition; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Imaging, Three-Dimensional - methods; Localization; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine; Medicine & Public Health; Methods; Myocardium; Neural networks; Neural Networks, Computer; Radiology; Statistical shape models; China; Deep learning; Cardiac image analysis; Statistical shape models; Complex transformation
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-025-01683-4

Background Cardiovascular diseases are the number one cause of death globally, making cardiac magnetic resonance image segmentation a popular research topic. Existing schemas relying on manual user interaction or semi-automatic segmentation are infeasible when dealing thousands of cardiac MRI studies. Thus, we proposed a full automatic and robust algorithm for large-scale cardiac MRI segmentation by combining the advantages of deep learning localization and 3D-ASM restriction. Material and methods The proposed method comprises several key techniques: 1) a hybrid network integrating CNNs and Transformer as a encoder with the EFG (Edge feature guidance) module (named as CTr-HNs) to localize the target regions of the cardiac on MRI images, 2) initial shape acquisition by alignment of coarse segmentation contours to the initial surface model of 3D-ASM, 3) refinement of the initial shape to cover all slices of MRI in the short axis by complex transformation. The datasets used are from the UK BioBank and the CAP (Cardiac Atlas Project). In cardiac coarse segmentation experiments on MR images, Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) are used to evaluate segmentation performance. In SPASM experiments, Point-to-surface (P2S) distances, Dice score are compared between automatic results and ground truth. Results The CTr-HNs from our proposed method achieves Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) of 0.95, 0.10 and 1.54 for the LV segmentation respectively, 0.88, 0.13 and 1.94 for the LV myocardium segmentation, and 0.91, 0.24 and 3.25 for the RV segmentation. The overall P2S errors from our proposed schema is 1.45 mm. For endocardium and epicardium, the Dice scores are 0.87 and 0.91 respectively. Conclusions Our experimental results show that the proposed schema can automatically analyze large-scale quantification from population cardiac images with robustness and accuracy.