.png){kind=icon}
.png){kind=icon}
Return
SAS-SemiUNet plus plus : A Stochastic Consistency Regularized Framework with Scale-Aware Semantic Recalibration for Cardiac MRI Segmentation
.png){kind=icon}
PRE
AI
R
M
L
DOI：10.3390/app16073507.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Featured Application The proposed SAS-SemiUNet++ framework enables high-precision segmentation of cardiac substructures (left/right ventricle, myocardium) from cardiac cine-MRI images and can be directly applied as a computer-aided diagnosis tool for clinical cardiovascular disease assessment (e.g., cardiomyopathy, myocardial infarction), supporting disease diagnosis, personalized treatment planning and prognostic evaluation.Abstract Precise segmentation of cardiac substructures in magnetic resonance imaging is pivotal for diagnosis and treatment planning but remains impeded by anatomical scale heterogeneity and the scarcity of high-quality pixel-level annotations. Existing deep learning paradigms often struggle to simultaneously resolve the global geometry of ventricular cavities and the fine-grained boundaries of the myocardium, particularly in low-data regimes. To address these challenges, we propose SAS-SemiUNet++, a holistic semi-supervised segmentation framework. This architecture incorporates two novel mechanisms: (1) The Scale-Aware Semantic Recalibration (SASR) unit, which functions as a dynamic semantic gate to adaptively adjust receptive fields, mimicking a radiologist's variable-focus mechanism to capture multi-scale anatomical details, and (2) Stochastic Consistency Regularization (SCR), a dual-path perturbation strategy that enforces geometric invariance on unlabeled data, thereby mitigating overfitting to noisy pseudo-labels. Comprehensive evaluations on the ACDC benchmark demonstrate that SAS-SemiUNet++ significantly outperforms state-of-the-art methods, achieving superior segmentation accuracy and boundary fidelity, particularly in reducing the 95% Hausdorff distance. This study presents a data-efficient and robust solution for cardiac image analysis, offering potential for scalable clinical deployment.
Keywords:
cardiac MRI segmentation
scale-aware semantic recalibration
stochastic consistency regularization
semi-supervised medical image analysis
Journal
A
IF:
2.5
Papers:
5.9K
Citations:
4
Organization
Cited Papers