CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

• URL: http://arxiv.org/abs/2304.09115v1
• Date: Tue, 18 Apr 2023 16:28:02 GMT
• Title: CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection
• Authors: Jiasheng Xu, Tianyi Zhang, Yangqian Wu, Jie Yang, Guang-Zhong Yang, Yun Gu
• Abstract summary: Cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens. Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Guided Feature Fusion (GFF) module is designed to supervise the model for adaptive feature fusion.
• Score: 36.048976865785605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

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