Publications

@article{banach_computer-based_2023,

title = {Computer-based airway stenosis quantification from bronchoscopic images: preliminary results from a feasibility trial},

author = {Artur Banach and Masahito Naito and Franklin King and Fumitaro Masaki and Hisashi Tsukada and Nobuhiko Hata},

doi = {10.1007/s11548-022-02808-8},

issn = {1861-6410, 1861-6429},

year  = {2023},

date = {2023-04-01},

journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY},

volume = {18},

number = {4},

pages = {707–713},

abstract = {Purpose Airway Stenosis (AS) is a condition of airway narrowing in the expiration phase. Bronchoscopy is a minimally invasive pulmonary procedure used to diagnose and/or treat AS. The AS quantification in a form of the Stenosis Index (SI), whether subjective or digital, is necessary for the physician to decide on the most appropriate form of treatment. The literature reports that the subjective SI estimation is inaccurate. In this paper, we propose an approach to quantify the SI defining the level of airway narrowing, using depth estimation from a bronchoscopic image. Methods In this approach we combined a generative depth estimation technique combined with depth thresholding to provide Computer-based AS quantification. We performed an interim clinical analysis by comparing AS quantification performance of three expert bronchoscopists against the proposed Computer-based method on seven patient datasets. Results The Mean Absolute Error of the subjective Human-based and the proposed Computer-based SI estimation was 24.0 +/- 13.9 [%] and 9.1 +/- 7.7 [%], respectively. The correlation coefficients between the CT measurements were used as the gold standard, and the Human-based and Computer-based SI estimation were-0.05 and 0.46, respectively. Conclusions We presented a new computer method to quantify the severity of AS in bronchoscopy using depth estimation and compared the performance of the method against a human-based approach. The obtained results suggest that the proposed Computer-based AS quantification is a feasible tool that has the potential to provide significant assistance to physicians in bronchoscopy.},

note = {Num Pages: 7 

Place: Heidelberg 

Publisher: Springer Heidelberg 

Web of Science ID: WOS:000900069100001},

keywords = {ADULTS, Airway stenosis quantification, Bronchoscopy, DEPTH, Image-guided surgery, OBSTRUCTION, RECONSTRUCTION, TRACHEOBRONCHOMALACIA},

pubstate = {published},

tppubtype = {article}

}

@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}

}