2021
Banach, Artur; King, Franklin; Masaki, Fumitaro; Tsukada, Hisashi; Hata, Nobuhiko
Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation Journal Article
In: MEDICAL IMAGE ANALYSIS, vol. 73, pp. 102164, 2021, ISSN: 1361-8415, 1361-8423, (Num Pages: 12 Place: Amsterdam Publisher: Elsevier Web of Science ID: WOS:000701725200004).
Abstract | Links | BibTeX | Tags: Bronchoscopy, CANCER, CT Imaging, DIAGNOSTIC BRONCHOSCOPY, guidance, GUIDED BRONCHOSCOPY, Image-guided surgery, Lung cancer, Motion tracking, NODULES, PERIPHERAL LUNG LESIONS, RECONSTRUCTION, SYSTEM, VIDEO REGISTRATION
@article{banach_visually_2021,
title = {Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation},
author = {Artur Banach and Franklin King and Fumitaro Masaki and Hisashi Tsukada and Nobuhiko Hata},
doi = {10.1016/j.media.2021.102164},
issn = {1361-8415, 1361-8423},
year = {2021},
date = {2021-10-01},
journal = {MEDICAL IMAGE ANALYSIS},
volume = {73},
pages = {102164},
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. (c) 2021 Elsevier B.V. All rights reserved.},
note = {Num Pages: 12
Place: Amsterdam
Publisher: Elsevier
Web of Science ID: WOS:000701725200004},
keywords = {Bronchoscopy, CANCER, CT Imaging, DIAGNOSTIC BRONCHOSCOPY, guidance, GUIDED BRONCHOSCOPY, Image-guided surgery, Lung cancer, Motion tracking, NODULES, PERIPHERAL LUNG LESIONS, RECONSTRUCTION, SYSTEM, VIDEO REGISTRATION},
pubstate = {published},
tppubtype = {article}
}
[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. (c) 2021 Elsevier B.V. All rights reserved.