Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate labeling. Existing software packages, such as FSL and FreeSurfer, do not fully replace ground truth segmentation, highlighting the need for an efficient segmentation tool. To better capture the essence of cerebral tissue, we introduce nnSegNeXt, an innovative segmentation architecture built upon the foundations of quality assessment. This pioneering framework effectively addresses the challenges posed by missing and inaccurate annotations. To enhance the model’s discriminative capacity, we integrate a 3D convolutional attention mechanism instead of conventional convolutional blocks, enabling simultaneous encoding of contextual information through the incorporation of multiscale convolutional features. Our methodology was evaluated on four multi-site T1-weighted MRI datasets from diverse sources, magnetic field strengths, scanning parameters, temporal instances, and neuropsychiatric conditions. Empirical evaluations on the HCP, SALD, and IXI datasets reveal that nnSegNeXt surpasses the esteemed nnUNet, achieving Dice coefficients of 0.992, 0.987, and 0.989, respectively, and demonstrating superior generalizability across four distinct projects with Dice coefficients ranging from 0.967 to 0.983. Additionally, extensive ablation studies have been implemented to corroborate the effectiveness of the proposed model. These findings represent a notable advancement in brain tissue analysis, suggesting that nnSegNeXt holds the promise to significantly refine clinical workflows.

The heart exhibits inherently nonlinear and chaotic electrical dynamics, making the early detection of myocardial ischemia (MI) challenging using traditional electrocardiography (ECG) or standard magnetocardiography (MCG). In this study, we propose an engineering-oriented framework that integrates classical nonlinear dynamics with machine-learning-based analysis, termed the Magnetocardiography Chaotic Dynamics Map (MCDM), to reconstruct nonlinear phase-space trajectories from 36-channel MCG recordings and capture differences in reconstructed nonlinear dynamics associated with ischemic conditions. Morphological and quantitative analyses of the MCDM patterns reveal marked differences between healthy and ischemic subjects. Using a machine-learning classifier trained on HOG and LBP descriptors, the proposed MCDM-based model achieved an accuracy of 92.19%, a sensitivity of 88.75%, a specificity of 95.63%, an F1-score of 91.91%, and an AUC of 89.80%, demonstrating effective discriminative capability for early ischemia screening. Owing to its computational simplicity and noninvasive nature, the proposed MCDM framework represents a promising tool for scalable screening of ischemic heart disease.