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 numerous state-of-the-art methods. Additionally, some lung organs are easily lost during the recycled down/up-sample procedure, e.g., bronchioles and arterioles, which can cause severe discontinuity issue. Inspired by these, this paper introduces an effective lung organ segmentation method called Fuzzy Attention-based Border Rendering Feature Orthogonal (FA-BRFO) network, which (1) integrates an efficient transformer-like fuzzy-attention module into deep networks to cope with the uncertainty in feature representations; (2) decouples and depicts the lung organ regions as cube-trees by focusing only on recycle-sampling border vulnerable points, rendering the severely discontinuous, false-negative/positive organ regions with two novel Global-Local Cube-tree Fusion and Sparse Patched Feature Orthogonal modules; (3) develops a Multi-scale Self-Knowledge Guidance module to improve model performance and robustness. We have demonstrated the efficacy of proposed method on five challenging datasets of lung organ segmentation, i.e., airway and artery. All experimental results demonstrate that our method can achieve the favorable performance significantly.

中文翻译：

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用于肺器官分割的基于模糊注意力的边界渲染正交网络


CT 图像上的自动肺器官分割对于肺部疾病的诊断至关重要。然而，无限的体素值和肺器官的类别不平衡可能会导致许多最先进方法中的假阴性/阳性和泄漏问题。此外，在回收的下/上采样过程中，一些肺器官很容易丢失，例如细支气管和小动脉，这可能导致严重的不连续性问题。受这些启发，本文介绍了一种有效的肺器官分割方法，称为基于模糊注意的边界渲染特征正交（FA-BRFO）网络，该方法（1）将高效的类似变压器的模糊注意模块集成到深度网络中以应对特征表示的不确定性； (2) 通过仅关注循环采样边界脆弱点，将肺器官区域解耦并描绘为立方体树，用两种新颖的全局局部立方体树融合和稀疏修补渲染严重不连续、假阴性/阳性的器官区域特征正交模块； (3)开发多尺度自知识指导模块以提高模型性能和鲁棒性。我们已经在五个具有挑战性的肺器官分割数据集（即气道和动脉）上证明了所提出的方法的有效性。所有实验结果都表明我们的方法可以显着取得良好的性能。