Deep monocular 3D reconstruction for assisted navigation in bronchoscopy

International Journal of Computer Assisted Radiology and Surgery
Marco Visentini-ScarzanellaShinichiro Koto

Abstract

In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific CT data. A fully convolutional network architecture is implemented on GPU and tested on a phantom dataset involving 32 video sequences and [Formula: see text]60k frames with aligned ground truth and renderings, which is made available as the first public dataset for bronchoscopy navigation. An average estimated depth accuracy of 1.5 mm was obtained, outperforming conventional direct depth estimation from input frames by 60%, and with a computational time of [Formula: see text]30 ms on modern GPUs. Qualitatively, the estimated depth and renderings closely resemble the ground truth. The proposed method shows a novel architecture to perform real-time monocular depth estimation without losing patient specificity in bronchoscopy. Future work will include integration within SLAM systems and collection of in vivo datasets.

References

Apr 19, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Richard SzeliskiCarsten Rother
Jan 1, 2009·Medical Image Computing and Computer-assisted Intervention : MICCAI·Marco Visentini-ScarzanellaGuang-Zhong Yang
Aug 31, 2010·Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer·Ralf EberhardtFelix J F Herth
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Dec 15, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Fayao LiuIan Reid
Jan 9, 2016·CA: a Cancer Journal for Clinicians·Rebecca L SiegelAhmedin Jemal

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Citations

Jun 13, 2019·International Journal of Computer Assisted Radiology and Surgery·Mathias UnberathNassir Navab
Apr 17, 2019·International Journal of Computer Assisted Radiology and Surgery·Anita RauDanail Stoyanov
Jun 4, 2020·Journal of Clinical Medicine·Gastone CiutiAnastasios Koulaouzidis
Jul 5, 2021·International Journal of Computer Assisted Radiology and Surgery·Artur BanachAaron McFadyen

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