Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
<|reference_start|>Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation: 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.<|reference_end|>
@article{zhang2024fuzzy,
    title={Fuzzy Attention-based Border Rendering Network for Lung Organ
  Segmentation},
    author={Sheng Zhang, Yang Nan, Yingying Fang, Shiyi Wang, Xiaodan Xing, Zhifan
  Gao, Guang Yang},
    journal={arXiv preprint arXiv:2406.16189},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16189},
    primaryClass={eess.IV cs.CV}
}