This article presents a general Bayesian learning framework for multi-modal groupwise image registration. The method builds on probabilistic modelling of the image generative process, where the underlying common anatomy and geometric variations of the observed images are explicitly disentangled as latent variables. Therefore, groupwise image registration is achieved via hierarchical Bayesian inference. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables, where the registration parameters can be explicitly estimated in a mathematically interpretable fashion. Remarkably, this new paradigm learns groupwise image registration in an unsupervised closed-loop self-reconstruction process, sparing the burden of designing complex image-based similarity measures. The computationally efficient disentangled network architecture is also inherently scalable and flexible, allowing for groupwise registration on large-scale image groups with variable sizes. Furthermore, the inferred structural representations from multi-modal images via disentanglement learning are capable of capturing the latent anatomy of the observations with visual semantics. Extensive experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images. The results have demonstrated the superiority of our method over conventional similarity-based approaches in terms of accuracy, efficiency, scalability, and interpretability.

Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without relying on explicit cross-domain alignment strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. Beyond quantitative improvement, we demonstrate strong interpretability of the proposed framework via manifold traversal for smooth shape manipulation. The results provide a principled foundation for anatomically informed, interpretable, and unified solutions for domain adaptation in medical imaging. The code is available at https://github.com/wxdrizzle/remind.

While yarn-level garments offer rich dynamic details and compelling visual realism compared to triangle-based models, their wide adoption is hindered by the immense computational cost due to the presence of a large number of degrees of freedom (DOFs). This paper proposes a novel simulation framework designed to enhance the performance and stability for numerical simulation of nonlinear, non-convex, and high-resolution knitwear. Our method generalizes the Douglas-Rachford Splitting (DRS) scheme to resolve the non-convex coupling between stretching, shearing, bending, twisting, and contacting at each yarn thread. A key contribution is a nested decomposition strategy that decouples the non-convex variational energy into independent and convex sub-problems. Such convexification improves solver robustness and removes the necessity for frequent line searches. We provide a theoretically grounded strategy for metric selection for each sub-problem, derived from an analysis of the convergence guarantee of DRS. Consequently, our method achieves close-to-optimal convergence along the nonlinear iterations rather than relying on ad-hoc parameter tuning. The paper also clarifies a formal connection between our generalized DRS and the ADMM (Alternating Direction Method of Multipliers) framework, extending the applicability of our analysis to a broader set of constrained dynamics problems. Experimental results demonstrate that our method robustly handles complex knitwear simulation scenes with superior efficiency, stability, and physical fidelity compared to existing methods. With a matrix-free GPU parallelization, our method allows an interactive simulation rate of knitwear of multi-million DOFs.

While total intracranial carotid artery calcification (ICAC) volume is an established stroke biomarker, growing evidence shows this aggregate metric ignores the critical influence of plaque location, since calcification in different segments carries distinct prognostic and procedural risks. However, a finer-grained, segment-specific quantification has remained technically infeasible. Conventional 3D models are forced to process downsampled volumes or isolated patches, sacrificing the global context required to resolve anatomical ambiguity and render reliable landmark localization. To overcome this, we reformulate the 3D challenge as a Parallel Probabilistic Landmark Localization task along the 1D axial dimension. We propose the Depth-Sequence Transformer (DST), a framework that processes full-resolution CT volumes as sequences of 2D slices, learning to predict$N=6$independent probability distributions that pinpoint key anatomical landmarks. Our DST framework demonstrates exceptional accuracy and robustness. Evaluated on a 100-patient clinical cohort with rigorous 5-fold cross-validation, it achieves a Mean Absolute Error (MAE) of 0.1 slices, with$96\%$of predictions falling within a$\pm 1$slice tolerance. Our work delivers the first practical tool for automated segment-specific ICAC analysis. The proposed framework provides a foundation for further studies on the role of location-specific biomarkers in diagnosis, prognosis, and procedural planning. Our code will be made publicly available.

In parallel simulation, convergence and parallelism are often seen as inherently conflicting objectives. Improved parallelism typically entails lighter local computation and weaker coupling, which unavoidably slow the global convergence. This paper presents a novel GPU algorithm that achieves convergence rates comparable to fullspace Newton's method while maintaining good parallelizability just like the Jacobi method. Our approach is built on a key insight into the phenomenon of overshoot. Overshoot occurs when a local solver aggressively minimizes its local energy without accounting for the global context, resulting in a local update that undermines global convergence. To address this, we derive a theoretically second-order optimal solution to mitigate overshoot. Furthermore, we adapt this solution into a pre-computable form. Leveraging Cubature sampling, our runtime cost is only marginally higher than the Jacobi method, yet our algorithm converges nearly quadratically as Newton's method. We also introduce a novel full-coordinate formulation for more efficient pre-computation. Our method integrates seamlessly with the incremental potential contact method and achieves second-order convergence for both stiff and soft materials. Experimental results demonstrate that our approach delivers high-quality simulations and outperforms state-of-the-art GPU methods with 50× to 100× better convergence.

This paper pushes the performance of cloth simulation, making the simulation interactive even for high-resolution garment models while keeping every triangle untangled. The penetration-free guarantee is inspired by the interior point method, which converts the inequality constraints to barrier potentials. We propose a major overhaul of this modality within the projective dynamics framework by leveraging an adaptive weighting mechanism inspired by barrier formulation. This approach does not depend on the distance between mesh primitives, but on the virtual life span of a collision event and thus keeps all the vertices within feasible region. Such a non-distance barrier model allows a new way to integrate collision resolution into the simulation pipeline. Another contributor to the performance boost comes from the subspace reuse strategy. This is based on the observation that low-frequency strain propagation is near orthogonal to the deformation induced by collisions or self-collisions, often of high frequency. Subspace reuse then takes care of low-frequency residuals, while high-frequency residuals can also be effectively smoothed by GPU-based iterative solvers. We show that our method outperforms existing fast cloth simulators by at least one order while producing high-quality animations of high-resolution models.

This paper presents volumetric homogenization, a spatially varying homogenization scheme for knitwear simulation. We are motivated by the observation that macro-scale fabric dynamics is strongly correlated with its underlying knitting patterns. Therefore, homogenization towards a single material is less effective when the knitting is complex and non-repetitive. Our method tackles this challenge by homogenizing the yarn-level material locally at volumetric elements. Assigning a virtual volume of a knitting structure enables us to model bending and twisting effects via a simple volume-preserving penalty and thus effectively alleviates the material nonlinearity. We employ an adjoint Gauss-Newton formulation[Zehnder et al. 2021] to battle the dimensionality challenge of such per-element material optimization. This intuitive material model makes the forward simulation GPU-friendly. To this end, our pipeline also equips a novel domain-decomposed subspace solver crafted for GPU projective dynamics, which makes our simulator hundreds of times faster than the yarn-level simulator. Experiments validate the capability and effectiveness of volumetric homogenization. Our method produces realistic animations of knitwear matching the quality of full-scale yarn-level simulations. It is also orders of magnitude faster than existing homogenization techniques in both the training and simulation stages.

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this paper, we trained an 8-layer CAE to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. After parameter optimization and prediction refinement, our trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.

Carotid plaques may cause strokes. The composition of the plaque helps assessing the risk. Magnetic resonance imaging (MRI) is a powerful technology for analyzing the composition. It is both tedious and error-prone for a human radiologist to review such images. Traditional computer-aided diagnosis tools use manually crafted features that lack both generality and accuracy. We propose a novel approach using Deep convolutional neural networks (CNN) to classify these plaque tissues. In order to accommodate the multi-contrast MRI images, we modify stateof-the-art CNN models to support different number of input channels, and also adapt the models to do pixel- wise predictions. On a dataset with 1,098 human subjects, we show that we achieve significantly better accuracy than previous models. Our result also indicates interesting relations between contrast weightings and tissue types

BACKGROUND: Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method. RESULTS: Different from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration. CONCLUSIONS: The proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

The aims of this study are (1) to propose an image segmentation framework for multiple contrast weighting MR images; and (2) to analyze the agreement between segmentation results and histology sections. The proposed technique is actually based on the mean-shift density estimation algorithm and carefully designed to overcome the drawbacks in other existing methods. First, it has a very reliable and accurate initialization scheme that guarantees all potential clusters are within estimation. Secondly, the proposed method introduces a dynamic sphere mechanism that makes the mean-shift vector more reliable even with poor initialization. It can also enhance the accuracy of cluster center estimation when the searching processing is approaching the mode. Experimental results and comparison with histology sections demonstrate its encouraging performance. Moreover, the proposed approach can be easily extended to other general-purpose low-level image analysis problems.

A new minimal path active contour model for boundary extraction is presented. Implementing the new approach requires four steps (1) users place some initial end points on or near the desired boundary through an interactive interface; (2) a potential searching window is defined between two end points; (3) a graph search method based on conic curves is used to search the boundary; and (4) a "wriggling" procedure is used to calibrate the contour and reduce sensitivity of the search results on the selected initial end points. The last three steps are performed automatically. In the proposed approach, the potential window systematically provides a new node connection for the later graph search, which is different from the row-by-row and column-by-column methods used in the classical graph search. Furthermore, this graph search also suggests ways to design a "wriggling" procedure to evolve the contour in the direction nearly perpendicular to itself by creating a list of displacement vectors in the potential window. The proposed minimal path active contour model speeds up the search and reduces the "metrication error" frequently encountered in the classical graph search methods e.g., the dynamic programming minimal path (DPMP) method.

Carotid artery vessel tracking and lumen segmentation is an important task for atherosclerotic plaque study, where the active contour model Snake has become one of the most powerful techniques for this purpose. However, its intrinsic weakness in initialization and topology changes handling limits its application in complicated situations. In this research, we focus our work on atherosclerotic blood vessel tracking and lumen contour segmentation of magnetic resource (MR) image sequences, along which the blood vessel bifurcates at indefinite position. To automatically capture this topology change in the processing, a practical solution is proposed by extending our previous research work. The procedure first presegments each MR slice into regions, and then uses a decision tree to track the blood vessel's topology change. Finally, the minimal path Snake (MPS) algorithm is applied to further search optimal lumen contours. Some experimental results in the preliminary study are provided to demonstrate its encouraging performance.

Magnetic resonance (MR) imaging and analysis have become one of the most important tools in medical research and clinical applications. To quantitatively evaluate the quality of MR images, some research work has been done previously. However, most of the research was only based on 2D spin echo images which can hardly be applied to atherosclerosis study. In this paper, we apply Shannon's information theory for optimal MR imaging and lesion index analysis. First, we review the information content (IC) which is used as a subjective criterion of image quality measurement in our work. Then we extend the existing spatial spectrum model to the 3D time-of-flight (ToF) imaging technique and find the optimal imaging resolution with which the maximum image information content can be obtained. A theoretical proof of its uniqueness is also given. At last, a lesion index system is proposed and developed for the blood vessel wall plaque analysis in our study. It is used to determine atherosclerotic lesion complexity and identify lesion changes over time. Some phantom and in vivo MR images are analyzed along with examples to demonstrate the performance.

In this paper, we propose a novel boundary detection approach for three-dimensional shape modeling. Our method is based on finding surfaces of minimal weighted area in a Riemannian metric. In order to take advantage of intensity information of images, we further integrate this intensity information into the boundary detection algorithm. We apply this algorithm to identify the inner and outer boundaries of the blood vessel wall in magnetic resonance images, and assess its accuracy and reproducibility. Our algorithm is reasonably accurate (about 2% difference in comparison with the manual method) and highly reproducible.

A knowledge based system has been designed for identifying and measuring fibrous cap (FC) in magnetic resonance images (MRI). The proposed method consists of three basic procedures. First, we apply Markov random field (MRF) to segment the whole image into regions and automatically identify the inner boundary of FC based on prior knowledge of lumen area. An enhanced algorithm, called QHCF, is proposed to find the optimal region segmentation. In the second step, active contour model is employed in finding FC's outer boundary. In this step, we use a new scheme which uses radial searching to find the end points so as to automatically initialize the contour tracking process. Finally, a thickness transformation is designed by extending binary mathematical morphology operation. Based on it, all the thickness related parameters can thus be measured quantitatively and consistently. Through these three integrated steps, FC is identified and measured. Experimental result demonstrates the encouraging performance of this algorithm.

Motion of the subject during magnetic resonance scan produces artifacts in the reconstructed images, which appear as blurring and ghost repetitions of the moving structures for the 2-dimensional Fourier Transform imaging methods. Several mathematical techniques have been proposed to correct the motion artifacts. Those techniques usually assume some types of motion models such as translational motion, rotational motion or linear expansion. In reality, motion can be far more complex, thus those techniques can be applied only in very limited cases. We present a new iterative algorithm to correct the corrupted data. Our method can be generalized to any arbitrary motion. The computer simulations demonstrate that a significant amount of improvement in motion artifact correction is achieved using this algorithm.>