arXiv subjects

Tetsuro Sekine

Publications and source records attributed to Tetsuro Sekine.

3 recordsLinked to original sources

arXiv2026

Segmentation of Gray Matters and White Matters from Brain MRI data

Accurate segmentation of brain tissues such as gray matter and white matter from magnetic resonance imaging is essential for studying brain anatomy, diagnosing neurological disorders, and monitoring disease progression. Traditional methods, such as FSL FAST, produce tissue probability maps but often require task-specific adjustments and face challenges with diverse imaging conditions. Recent foundation models, such as MedSAM, offer a prompt-based approach that leverages large-scale pretraining. In this paper, we propose a modified MedSAM model designed for multi-class brain tissue segmentation. Our preprocessing pipeline includes skull stripping with FSL BET, tissue probability mapping with FSL FAST, and converting these into 2D axial, sagittal, coronal slices with multi-class labels (background, gray matter, and white matter). We extend MedSAM's mask decoder to three classes, freezing the pre-trained image encoder and fine-tuning the prompt encoder and decoder. Experiments on the IXI dataset achieve Dice scores up to 0.8751. This work demonstrates that foundation models like MedSAM can be adapted for multi-class medical image segmentation with minimal architectural modifications. Our findings suggest that such models can be extended to more diverse medical imaging scenarios in future work.

cs.CV↗

arXiv2026

Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks

This work introduces an unsupervised Divergence and Aliasing-Free neural network (DAF-FlowNet) for 4D Flow Magnetic Resonance Imaging (4D Flow MRI) that jointly enhances noisy velocity fields and corrects phase wrapping artifacts. DAF-FlowNet parameterizes velocities as the curl of a vector potential, enforcing mass conservation by construction and avoiding explicit divergence-penalty tuning. A cosine data-consistency loss enables simultaneous denoising and unwrapping from wrapped phase images. On synthetic aortic 4D Flow MRI generated from computational fluid dynamics, DAF-FlowNet achieved lower errors than existing techniques (up to 11% lower velocity normalized root mean square error, 11% lower directional error, and 44% lower divergence relative to the best-performing alternative across noise levels), with robustness to moderate segmentation perturbations. For unwrapping, at peak velocity/velocity-encoding ratios of 1.4 and 2.1, DAF-FlowNet achieved 0.18% and 5.2% residual wrapped voxels, representing reductions of 72% and 18% relative to the best alternative method, respectively. In scenarios with both noise and aliasing, the proposed single-stage formulation outperformed a state-of-the-art sequential pipeline (up to 15% lower velocity normalized root mean square error, 11% lower directional error, and 28% lower divergence). Across 10 hypertrophic cardiomyopathy patient datasets, DAF-FlowNet preserved fine-scale flow features, corrected aliased regions, and improved internal flow consistency, as indicated by reduced inter-plane flow bias in aortic and pulmonary mass-conservation analyses recommended by the 4D Flow MRI consensus guidelines. These results support DAF-FlowNet as a framework that unifies velocity enhancement and phase unwrapping to improve the reliability of cardiovascular 4D Flow MRI.

cs.LG↗

arXiv2025

Enhancing Hemodynamic Parameter Estimations: Nonlinear Blood Behavior in 4D Flow MRI

Hemodynamic parameters are often estimated assuming a constant Newtonian viscosity, even though blood exhibits shear-thinning behavior. This article investigates the influence of blood rheology and hematocrit (Hct) percentage on the estimation of Wall Shear Stress (WSS), rate of viscous Energy Loss ($\dot{E}_L$) at different points in the cardiac cycle, and the Oscillatory Shear Index (OSI). We focus on a hematocrit-dependent power-law non-Newtonian model, considering a wide range of Hct values at physiological temperature, with rheological parameters obtained from previously reported experimental data. In all cases, we systematically compared WSS, $\dot{E}_L$, and OSI using both Newtonian and power-law models, underscoring the crucial role of blood rheology in accurately assessing cardiovascular diseases. Our results show that, in in-silico experiments, differences in WSS and $\dot{E}_L$ across a wide range of Hct values can reach as high as 190\% and 113\% at systole, and as low as -72\% and -74\% at diastole, respectively. In in-vivo data, differences in WSS and $\dot{E}_L$ can reach up to -45\% and -60\% at systole, and range from -69\% to 73\% at diastole. This study enhances our understanding of the impact of blood rheology on hemodynamic parameter estimations using both in-silico and in-vivo aortic 4D Flow MRI data.

eess.SP↗