spatial prior integration

Traditional mathematical priors struggle to capture complex anatomical structures in brain MRI inverse problems. Method: We propose a data-driven score-based diffusion model as a generic generative prior embedded within a Bayesian inversion framework. The approach requires no paired training data, seamlessly integrating the forward physical model and domain-specific knowledge to jointly address super-resolution, bias-field correction, and image inpainting. Contribution/Results: This work presents the first application of diffusion models as a universal prior in medical imaging inverse problems. By combining unsupervised learning with physics-based constraints, it ensures anatomical consistency. Evaluated on multi-center clinical and research MRI datasets, our method significantly outperforms conventional optimization-based techniques and state-of-the-art deep learning models, achieving new SOTA performance in reconstruction fidelity and structural integrity.

SAM faces critical challenges in medical image segmentation, including ill-defined boundaries, insufficient modeling of anatomical relationships, and lack of uncertainty quantification. To address these, we propose KG-SAM—a novel framework that first integrates an anatomical knowledge graph (KG) and an energy-driven conditional random field (CRF) into the SAM architecture. The KG encodes prior anatomical structural constraints, while the CRF jointly optimizes segmentation boundaries and models prediction uncertainty. An uncertainty-aware fusion module further enhances robustness. This design significantly improves segmentation consistency, interpretability, and clinical applicability. Extensive evaluation on multi-center datasets demonstrates state-of-the-art performance: Dice scores of 82.69% for prostate segmentation, and 78.05% and 79.68% for abdominal MRI and CT segmentation, respectively—each substantially surpassing existing methods.

Existing automated methods struggle to emulate the reasoning process orthodontists employ when localizing cephalometric landmarks based on anatomical structures and geometric rules. This work proposes a five-stage anatomy-guided initialization pipeline that, for the first time, encodes clinical anatomical knowledge as spatial attention priors and integrates them into an HRNet-W32 detector via confidence-weighted fusion. This inductive bias substantially enhances the model’s generalization capability. Evaluated on 1,502 multi-source cephalograms, the method achieves a mean radial error of 1.04 mm across 25 landmarks—representing a 15.4% improvement over the current state of the art—with 12 landmarks exhibiting sub-millimeter accuracy. Ablation studies confirm that the performance gain stems from improved anatomical plausibility rather than the addition of extra input channels.

To address the challenge of establishing stable inter-subject local correspondences in anatomical shape statistics—traditionally reliant on explicit registration—we propose a registration-free, boundary- and interior-consistent geometric modeling framework. Our method models target deformations as diffeomorphic transformations of an ellipsoid, embedded within a globally optimized skeleton-driven fitting scheme that simultaneously constructs a consistent coordinate system on both the object’s boundary and interior. We further introduce an evolutionary s-rep representation, the first to encode intrinsic geometric features directly in the fitted coordinate space, enabling robust point-wise correspondence across subjects without mesh alignment. The approach integrates differential-geometric deformation modeling, skeleton-guided fitting, and boundary-driven intrinsic coordinate generation. In hippocampal disease classification, it significantly outperforms two state-of-the-art methods, demonstrating superior discriminative power and statistical stability of the learned features.

Automatic pancreatic segmentation in abdominal CT suffers from low accuracy and high false-negative rates. Method: We propose a 3D full-resolution nnU-Net framework integrated with anatomical prior knowledge—specifically, multi-organ anatomical labels from TotalSegmentator, leveraging spatial constraints from neighboring organs as strong priors, and jointly trained end-to-end on the PANORAMA dataset. Contribution/Results: Our method significantly improves segmentation robustness: Dice score increases by 6.0% (p < 0.001), Hausdorff distance decreases by 36.5 mm (p < 0.001), and achieves 100% pancreatic detection with zero false negatives. This work empirically validates the critical value of anatomical priors for fine-grained single-organ segmentation, establishing a highly reliable foundation for radiomic biomarker extraction and pancreatic lesion identification.

This study addresses the lack of a statistically rigorous framework in current neuroimaging research for testing hypotheses about associations between structural and functional brain data. The authors propose the first explicit Bayesian hypothesis testing approach that integrates fMRI-derived functional brain networks with regional structural measurements through a hierarchical Bayesian model. This method explicitly models the relationship between structure and function while providing full posterior uncertainty quantification. It facilitates the integration of heterogeneous data types and incorporation of prior information, demonstrating robust and efficient detection of structure–function associations across varying signal-to-noise ratios, numbers of brain regions, and types of structural measures. The proposed approach substantially outperforms existing methods in both accuracy and reliability.

This work proposes a covariate-conditioned probabilistic 3D shape model that integrates implicit neural representations with uncertainty-aware statistical shape analysis to address the dynamic modeling of anatomical shape heterogeneity during development and the quantification of its spatial uncertainty. The method introduces a closed-form Fisher information metric, enabling efficient analytical computation of local temporal uncertainties through automatic differentiation, thereby supporting spatially continuous and interpretable uncertainty modeling. Experiments on three synthetic datasets and one clinical dataset demonstrate that the proposed framework simultaneously achieves high-fidelity shape reconstruction and clinically interpretable uncertainty estimation.

This work addresses the challenges of interpretability and precise lesion localization in chest X-ray diagnosis by proposing a graph neural network framework that integrates anatomical structure priors. The method reformulates convolutional feature maps into patch-level graphs incorporating both appearance features and spatial coordinates, and introduces a tailored structural propagation mechanism that explicitly models relative anatomical relationships among nodes, thereby endowing the graph network with an inductive bias for structured reasoning. Intrinsic interpretability is achieved through node importance scoring and joint graph-node prediction, eliminating the need for post-hoc visualization. Experimental results demonstrate that the proposed approach effectively enhances both diagnostic accuracy and model interpretability, while exhibiting strong potential for cross-domain generalization.

Existing text-driven CT generation methods often lack anatomical guidance, leading to spatial blurriness or anatomical inconsistencies. To address this limitation, this work proposes a retrieval-augmented diffusion framework that leverages a 3D vision-language encoder to retrieve semantically relevant clinical cases from a database. The anatomical annotations of these retrieved cases serve as structural priors, which are integrated into a latent diffusion model via ControlNet to harmonize textual semantics with anatomical fidelity. Notably, this approach achieves spatial controllability and anatomical plausibility in text-to-CT synthesis without requiring ground-truth anatomical labels during inference. Evaluated on the CT-RATE dataset, the method demonstrates significant improvements in both image fidelity and clinical consistency compared to existing approaches.

This work addresses the dual challenges of label scarcity and cross-institutional domain shift, which severely limit the performance of existing few-shot segmentation methods and hinder fixed-weight ensembles from adapting to target domains. To overcome these limitations, the authors propose a Bayesian adaptive weighted ensemble framework: multiple few-shot segmentation models are first fine-tuned, and then Bayesian optimization is employed to automatically learn optimal fusion weights on a target-domain validation set, which are subsequently used for query image prediction. This approach represents the first application of Bayesian optimization to ensemble few-shot medical image segmentation, enabling adaptive weight assignment that accounts for both anatomical structures and institutional domain characteristics. Evaluated on the Cross-institution Male Pelvic Structures dataset, the method significantly outperforms single models, fixed ensembles, models trained from scratch, and current state-of-the-art approaches, demonstrating strong potential for clinical deployment.