HST-Net: Hierarchical Spectrum-Tokenization with Progressive Refinement for Cardiac MRI Segmentation - Naga Chandrika Gogulamudi; Shamia D; V Kavithamani; Amitha Ida Chandran; K Venu; Kunchanapalli Rama Krishna (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
The accurate segmentation of cardiac structures from Magnetic Resonance Imaging (MRI) plays a vital role in quantitative ventricular assessment, functional analysis, and the clinical diagnosis of cardiovascular diseases. Precise delineation of cardiac components, such as the left ventricle, right ventricle, and myocardial wall, is essential for evaluating cardiac morphology and function. In recent years, transformer-based architectures, including TransUNet and Swin-UNet, have demonstrated strong capabilities in modeling long-range dependencies and capturing global contextual information. However, despite these advantages, they often struggle to preserve smooth anatomical geometry and achieve high-precision boundary delineation, particularly in the presence of large shape deformations and significant inter-subject variability commonly observed in cardiac MRI data. To overcome these limitations, a Hierarchical Spectrum-Tokenization Network (HST-Net) is proposed. The core idea of HST-Net is to represent cardiac anatomy at multiple levels of granularity, enabling a more robust structural understanding across varying spatial scales. The proposed architecture incorporates a novel approach called Spectrum Tokenization. This approach divides the latent representations into two parts, one containing low-frequency global tokens that capture context information, and another containing high-frequency boundary-aware tokens that capture the contours. By progressively enhancing boundary details, PSR significantly improves contour accuracy, especially for complex and thin structures. Experimental evaluations conducted on a cardiac MRI dataset demonstrate the effectiveness of the proposed approach. HST-Net achieves an average Dice coefficient of 91.6% and a pixel-wise segmentation accuracy of 94.8%. Compared to nnU-Net and Swin-UNet, it shows consistent performance gains, yielding improvements of 2.1–3.4% in Dice score and 1.9–2.6% in segmentation accuracy across different cardiac structures.