ArticleInternational journal of computer assisted radiology and surgery2021
Depth-based branching level estimation for bronchoscopic navigation.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 15 citations in OpenAlex.
- Pose estimation via structure-depth information from monocular endoscopy images sequence.Biomedical optics express · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
- Landmark Based Bronchoscope Localization for Needle Insertion Under Respiratory Deformation.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2023Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
purposeBronchoscopists rely on navigation systems during bronchoscopy to reduce the risk of getting lost in the complex bronchial tree-like structure and the homogeneous bronchus lumens. We propose a patient-specific branching level estimation method for bronchoscopic navigation because it is vital to identify the branches being examined in the bronchus tree during examination.
methodsWe estimate the branching level by integrating the changes in the number of bronchial orifices and the camera motions among the frames. We extract the bronchial orifice regions from a depth image, which is generated using a cycle generative adversarial network (CycleGAN) from real bronchoscopic images. We calculate the number of orifice regions using the vertical and horizontal projection profiles of the depth images and obtain the camera-moving direction using the feature point-based camera motion estimation. The changes in the number of bronchial orifices are combined with the camera-moving direction to estimate the branching level.
resultsWe used three in vivo and one phantom case to train the CycleGAN model and four in vivo cases to validate the proposed method. We manually created the ground truth of the branching level. The experimental results showed that the proposed method can estimate the branching level with an average accuracy of 87.6%. The processing time per frame was about 61 ms.
conclusionExperimental results show that it is feasible to estimate the branching level using the number of bronchial orifices and camera-motion estimation from real bronchoscopic images.
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