Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets

Jumpei Nitta, Megumi Nakao, Keiho Imanishi, Tetsuya Matsuda
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations
more » ... m camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN.
doi:10.1109/embc44109.2020.9176214 pmid:33018221 fatcat:ymwsjquqvrbqdllkv575deojsy