Introduction: The methods employed to document cystoscopic findings in bladder cancer patients lack accuracy and are subject to observer variability. We propose a novel endoimaging system and an online documentation platform to provide post-procedural 3D bladder reconstructions for improved diagnosis, management and follow-up.
Material and methods: The RaVeNNA4pi consortium is comprised of five industrial partners, two university hospitals and two technical institutes. These are grouped into hardware, software and clinical partners according to their professional expertise. The envisaged endoimaging system consists of an innovative cystoscope that generates 3D bladder reconstructions allowing users to remotely access a cloud-based centralized database to visualize individualized 3D bladder models from previous cystoscopies archived in DICOM format.
Results: Preliminary investigations successfully tracked the endoscope's rotational and translational movements. The structure-from-motion pipeline was tested in a bladder phantom and satisfactorily demonstrated 3D reconstructions of the processing sequence. AI-based semantic image segmentation achieved a 0.67 dice-score-coefficient over all classes. An online-platform allows physicians and patients to digitally visualize endoscopic findings by navigating a 3D bladder model.
Conclusions: Our work demonstrates the current developments of a novel endoimaging system equipped with the potential to generate 3D bladder reconstructions from cystoscopy videos and AI-assisted automated detection of bladder tumors.
Keywords: 3D-reconstruction; Bladder cancer; artificial neural network; bladder phantom model; cystoscopy; semantic image segmentation.