Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation

• URL: http://arxiv.org/abs/2406.16189v2
• Date: Mon, 1 Jul 2024 09:34:22 GMT
• Title: Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
• Authors: Sheng Zhang, Yang Nan, Yingying Fang, Shiyi Wang, Xiaodan Xing, Zhifan Gao, Guang Yang,
• Abstract summary: This paper introduces an effective lung organ segmentation method called Fuzzy Attention-based Border Rendering (FABR) network. 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. All experimental results, on four challenging datasets of airway & artery, demonstrate that our method can achieve the favorable performance significantly.
• Score: 12.239237740592639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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