Depth Estimation for Flexible Ureteroscopic Images Based on Style-Transfer
Using flexible ureteroscopes is the mainstream treatment method for nephrolithiasis owing to their slender and flexible structure,allowing them to navigate through the narrow natural passages of the human body,such as the urethra and ureters.The elongated shape of these ureteroscopes provides a broad visual inspection range,enabling physicians to effectively observe affected areas.However,conventional ureteroscopes typically feature a single-camera system for surgical operations,leading to limited image data.This limitation results in insufficient informa-tion as additional imaging devices cannot be leveraged.Unlike surgical scenarios such as gastrointestinal or nasal en-doscopy,the kidney's internal environment lacks publicly available datasets.The images captured during surgery exhibit varying quality,with insufficient surface texture details,fewer folds in cavity areas,and susceptibility to interference such as blurriness and reflections.These challenges can significantly impact depth estimation.Herein,a depth estimation method is proposed,which involves leveraging an improved style-transfer model to address the aforementioned issues.The method begins by reconstructing the internal cavity model of the kidney based on preop-erative computed tomography images and extracting the central path.The involved virtual endoscope's camera is then positioned at interpolated path points,creating a dataset that correlates virtual endoscope roaming images with depth estimation images.A depth estimation model is trained using this dataset.Subsequently,an improved style-transfer model incorporating an efficient channel attention module is employed to transfer the real endoscopic image domain to the virtual endoscopic image domain.Finally,the virtual endoscopic images generated through style-transfer are in-put into the previously trained depth estimation model,achieving depth estimation of real endoscopic images.The feasibility and effectiveness of the proposed method are validated using images obtained from ureteroscopic holmium laser lithotripsy procedures.
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