Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation

Cheng Wang; Yuichiro Hayashi; Masahiro Oda; Takayuki Kitasaka; Hirotoshi Honma; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori; Kensaku Mori

doi:10.1117/12.2582341

15 February 2021 Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation

Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115980N (2021) https://doi.org/10.1117/12.2582341
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

This paper describes a bronchial orifice (BO) segmentation method on real bronchoscopic video frames by using depth images. The BO is one of the anatomical characteristics in the bronchus, which is critical in clinical applications such as bronchus scene description and navigation path generation. Previous work used image appearance and the gradation of the real bronchoscopic image to segment orifice region, which behaved poorly in complex scenes including bubble or changes in illumination. To obtain a better segmentation result of BO even in the complex scenes, we propose a BO segmentation method using the distance between the bronchoscope camera and the bronchus lumen, which is represented by a depth image. Since the depth image is unavailable due to devices limitation, we use an image-to-image domain translation network named cycle generative adversarial network (CycleGAN) to estimate depth images from real bronchoscopic images. The BO regions are considered as the regions whose distances are larger than a distance threshold. We decide the distance threshold according to the depth images' projection profiles. Experimental results showed that the proposed method can find BO regions in the real bronchoscopic videos in real-time. We manually labeled BO regions as ground truth to evaluate the proposed method. The average Dice score of the proposed method was 77.0 %.

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Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, and Kensaku Mori "Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980N (15 February 2021); https://doi.org/10.1117/12.2582341

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