Fangfang Xie

Minghui Zhang, Chenyu Li, Fangfang Xie et al.

Accurate anatomical labeling and analysis of the pulmonary structure and its surrounding anatomy from thoracic CT is getting increasingly important for understanding the etilogy of abnormalities or supporting targetted therapy and early interventions. Whilst lung and airway cell atlases have been attempted, there is a lack of fine-grained morphological atlases that are clinically deployable. In this work, we introduce AirMorph, a robust, end-to-end deep learning pipeline enabling fully automatic and comprehensive airway anatomical labeling at lobar, segmental, and subsegmental resolutions that can be used to create digital atlases of the lung. Evaluated across large-scale multi-center datasets comprising diverse pulmonary conditions, the AirMorph consistently outperformed existing segmentation and labeling methods in terms of accuracy, topological consistency, and completeness. To simplify clinical interpretation, we further introduce a compact anatomical signature quantifying critical morphological airway features, including stenosis, ectasia, tortuosity, divergence, length, and complexity. When applied to various pulmonary diseases such as pulmonary fibrosis, emphysema, atelectasis, consolidation, and reticular opacities, it demonstrates strong discriminative power, revealing disease-specific morphological patterns with high interpretability and explainability. Additionally, AirMorph supports efficient automated branching pattern analysis, potentially enhancing bronchoscopic navigation planning and procedural safety, offering a valuable clinical tool for improved diagnosis, targeted treatment, and personalized patient care.

Junyang Wu, Mingyi Luo, Fangfang Xie et al.

Accurate intraoperative navigation is essential for robot-assisted endoluminal intervention, but remains difficult because of limited endoscopic field of view and dynamic artifacts. Existing navigation platforms often rely on external localization technologies, such as electromagnetic tracking or shape sensing, which increase hardware complexity and remain vulnerable to intraoperative anatomical mismatch. We present a vision-only autonomy framework that performs long-horizon bronchoscopic navigation using preoperative CT-derived virtual targets and live endoscopic video, without external tracking during navigation. The framework uses hierarchical long-short agents: a short-term reactive agent for continuous low-latency motion control, and a long-term strategic agent for decision support at anatomically ambiguous points. When their recommendations conflict, a world-model critic predicts future visual states for candidate actions and selects the action whose predicted state best matches the target view. We evaluated the system in a high-fidelity airway phantom, three ex vivo porcine lungs, and a live porcine model. The system reached all planned segmental targets in the phantom, maintained 80\% success to the eighth generation ex vivo, and achieved in vivo navigation performance comparable to the expert bronchoscopist. These results support the preclinical feasibility of sensor-free autonomous bronchoscopic navigation.

Hao Shi, Xi Li, Fangfang Xie

Competitive Co-evolutionary Algorithms (CCEAs) are often hampered by complex dynamics like intransitivity and the Red Queen effect, leading to unstable convergence. To counter these challenges, this paper introduces the Marker Gene Method (MGM), a framework that establishes stability by using a 'marker gene' as a dynamic benchmark and an adaptive weighting mechanism to balance exploration and exploitation. We provide rigorous mathematical proofs demonstrating that MGM creates strong attractors near Nash Equilibria within the Strictly Competitive Game framework. Empirically, MGM demonstrates its efficacy across a spectrum of challenges: it stabilizes the canonical Rock-Paper-Scissors game, significantly improves the performance of C-RMOEA/D on ZDT benchmarks, and, when augmented with a Memory Pool (MP) extension, it successfully tames the notoriously pathological Shapley Biased Game. This work presents a theoretically sound and empirically validated framework that substantially enhances the stability and robustness of CCEAs in complex competitive environments.

Junyang Wu, Fangfang Xie, Jiayuan Sun et al.

Domain adaptation, which bridges the distributions across different modalities, plays a crucial role in multimodal medical image analysis. In endoscopic imaging, combining pre-operative data with intra-operative imaging is important for surgical planning and navigation. However, existing domain adaptation methods are hampered by distribution shift caused by in vivo artifacts, necessitating robust techniques for aligning noisy and artifact abundant patient endoscopic videos with clean virtual images reconstructed from pre-operative tomographic data for pose estimation during intraoperative guidance. This paper presents an artifact-resilient image translation method and an associated benchmark for this purpose. The method incorporates a novel ``local-global'' translation framework and a noise-resilient feature extraction strategy. For the former, it decouples the image translation process into a local step for feature denoising, and a global step for global style transfer. For feature extraction, a new contrastive learning strategy is proposed, which can extract noise-resilient features for establishing robust correspondence across domains. Detailed validation on both public and in-house clinical datasets has been conducted, demonstrating significantly improved performance compared to the current state-of-the-art.

Hao Zheng, Yulei Qin, Yun Gu et al.

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function which obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines.