ArticleMedical image analysis2021
Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Noninvasive renal mass diagnostic models based on radiomic features and clinical data: a multi-center study.Abdominal radiology (New York) · 2026Article
- Rimplasty: An Arthroscopic Technique for Patients With Isolated Patellar Chondromalacia Based on Autologous Matrix-induced Chondrogenesis+ and Limited Trochleoplasty.Arthroscopy techniques · 2026Article
- BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.International journal of computer assisted radiology and surgery · 2026Article
- A self-supervised depth-aware method for pose optimization in hybrid bronchoscopic navigation.Scientific reports · 2026Article
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Gia-Net: geometry-informed attention network for 3D point cloud registration in liver surgery.International journal of computer assisted radiology and surgery · 2026Article
- Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- An accurate prediction for respiratory diseases using deep learning on bronchoscopy diagnosis images.Journal of advanced research · 2025Article
- BronchoGAN: anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy.International journal of computer assisted radiology and surgery · 2025Article
- A Real-Time, Semi-Autonomous Navigation Platform for Soft Robotic Bronchoscopy.IEEE robotics and automation letters · 2025Article
- The intelligent development and preservation of folk sports culture under artificial intelligence.Scientific reports · 2025Article
- A bronchoscopic navigation method based on neural radiation fields.International journal of computer assisted radiology and surgery · 2024Article
- Airway label prediction in video bronchoscopy: capturing temporal dependencies utilizing anatomical knowledge.International journal of computer assisted radiology and surgery · 2024Article
- AI co-pilot bronchoscope robot.Nature communications · 2024Article
- A cGAN-based network for depth estimation from bronchoscopic images.International journal of computer assisted radiology and surgery · 2024Article
- Pose estimation via structure-depth information from monocular endoscopy images sequence.Biomedical optics express · 2024Article
- 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
- Diagnostic Performance of Electromagnetic Navigation versus Virtual Navigation Bronchoscopy-Guided Biopsy for Pulmonary Lesions in a Single Institution: Potential Role of Artificial Intelligence for Navigation Planning.Diagnostics (Basel, Switzerland) · 2023Article
- Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network.Biomedical optics express · 2022Article
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.