Atrial fibrillation (AF), the most common cardiac arrhythmia, affects one in three adults over 45 years of age. Improving its treatment requires a better understanding of bi-atrial anatomy. Existing benchmarks have focused on the left atrial (LA) cavity, overlooking the fundamental challenges posed by bi-atrial anatomy, most notably the thin atrial walls, which are critical for substrate-guided ablation planning in patients with atrial fibrillation. To address these limitations, the Multi-class Bi-Atrial Segmentation 2024 Challenge (MBAS2024) introduced the first large-scale, multi-class benchmark for simultaneous segmentation of the LA cavity, right atrial (RA) cavity, and bi-atrial walls from late gadolinium-enhanced (LGE) MRI. We systematically evaluated 13 state-of-the-art methods on the world's largest curated bi-atrial dataset, comprising 175 3D multi-center scans with expert-validated annotations, providing a comprehensive assessment of current methodological capabilities and limitations. Key findings include: segmentation of the LA and RA cavities is generally robust to image quality, whereas atrial wall delineation is highly sensitive to image degradation. Performance varies across centers, indicating limited generalization of atrial wall segmentation across different acquisition protocols. Model architecture, rather than hyperparameter tuning, is the primary driver of performance, with U-Net-based models and emerging state-space models (e.g., UMambaBot) achieving higher accuracy at modest computational cost. Segmentation accuracy also varies along the slice dimension, with central slices segmented more reliably. Finally, hybrid labeling strategies-separating LA and RA cavities while merging bi-atrial walls into a single class-consistently improve performance. The MBAS2024 challenge establishes a foundational benchmark for bi-atrial segmentation, providing validated baselines and actionable insights to guide the development of clinically relevant, efficient, and anatomically aware segmentation algorithms to improve targeted ablation in patients with AF.

The Fast Marching method is widely employed in several fields of image processing. Some years ago a Multi-Stencil version (MSFM) was introduced to improve its accuracy by solving the equation for a set of stencils and choosing the best solution at each considered node. The following work proposes a modified numerical scheme for MSFM to take into account the variation of the local cost, which has proven to be second order. The influence of the stencil set choice on the algorithm outcome with respect to stencil orthogonality and axis swapping is also explored, where stencils are taken from neighborhoods of varying radius. The experimental results show that the proposed schemes improve the accuracy of their original counterparts, and that the use of permutation-invariant stencil sets provides robustness against shifted vector coordinates in the stencil set.

We propose a fully 3-D methodology for the computation of myocardial nonviable tissue transmurality in contrast enhanced magnetic resonance images. The outcome is a continuous map defined within the myocardium where not only current state-of-the-art measures of transmurality can be calculated, but also information on the location of nonviable tissue is preserved. The computation is done by means of a partial differential equation framework we have called multi-stencil streamline fast marching. Using it, the myocardial and scarred tissue thickness is simultaneously computed. Experimental results show that the proposed 3-D method allows for the computation of transmurality in myocardial regions where current 2-D methods are not able to as conceived, and it also provides more robust and accurate results in situations where the assumptions on which current 2-D methods are based-i.e., there is a visible endocardial contour and its corresponding epicardial points lie on the same slice-, are not met.

This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.