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.

翻译：CT图像上的自动肺器官分割对于肺部疾病诊断至关重要。然而，肺器官无限制的体素值和类别不平衡可能导致先进方法中出现假阴性/阳性及泄漏问题。此外，一些细长的肺器官（如细支气管和小动脉）在循环下采样/上采样过程中容易丢失，导致严重的间断性问题。受此启发，本文提出了一种有效的肺器官分割方法，称为基于模糊注意力的边界渲染（FABR）网络。由于模糊逻辑能够处理特征提取中的不确定性，因此深度网络与模糊集合的融合应是提升性能的可行方案。同时，与先前在全部规则密集点上操作的一流方法不同，我们的FABR将肺器官区域描绘为立方体树，仅关注循环采样的边界脆弱点，并通过新颖的全局-局部立方体树融合（GLCF）模块渲染严重间断、假阴性/阳性的器官区域。在气道和动脉的四个具有挑战性的数据集上的所有实验结果均表明，我们的方法能够显著取得优越的性能。