Methods for 2D and 3D Endobronchial Ultrasound Image Segmentation

Methods for 2D and 3D Endobronchial Ultrasound Image Segmentation 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Xiaonan Zang, Rebecca Bascom, Christopher Gilbert, Jennifer Toth, and William E. Higgins, The Pennsylvania State University, USA


Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use, and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2D EBUS frames and 3D EBUS sequences. Our 2D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2D EBUS frames. Our 3D method builds upon the 2D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2D method achieved an overall Dice index = 90.0%±4.9%, while the 3D method achieved an overall Dice index = 83.9%±6.0%. In addition, the computation time (2D, 0.070 sec/frame; 3D, 0.088 sec/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

Key words: endobronchial ultrasound, image segmentation, bronchoscopy, image-guided intervention system, lung cancer

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