Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negative/positive and leakage issues in advanced methods. Additionally, some slender lung organs are easily lost during the recycled down/up-sample procedure, e.g., bronchioles & arterioles, causing severe discontinuity issue. Inspired by these, this paper introduces an effective lung organ segmentation method called Fuzzy Attention-based Border Rendering (FABR) network. Since fuzzy logic can handle the uncertainty in feature extraction, hence the fusion of deep networks and fuzzy sets should be a viable solution for better performance. Meanwhile, unlike prior top-tier methods that operate on all regular dense points, our FABR depicts lung organ regions as cube-trees, focusing only on recycle-sampled border vulnerable points, rendering the severely discontinuous, false-negative/positive organ regions with a novel Global-Local Cube-tree Fusion (GLCF) module. All experimental results, on four challenging datasets of airway & artery, demonstrate that our method can achieve the favorable performance significantly.

该研究介绍了一种名为模糊注意力边界渲染（FABR）网络的有效肺器官分割方法，该方法利用模糊逻辑处理特征提取中的不确定性，通过全局局部立方树融合（GLCF）模块，将肺器官区域描述为立方树，重点关注容易丢失的边界脆弱点，从而实现对严重不连续、假阴/假阳性器官区域的渲染，实验结果表明该方法在气道与动脉的四个具有挑战性的数据集上具有显著的性能优势。

基于模糊注意力的边界渲染网络用于肺器官分割