Abstract
White light endoscopy is widely used for diagnostic imaging of the interior of organs and body cavities, but the inability to correlate individual 2D images with 3D organ morphology limits its utility for quantitative or longitudinal studies of disease physiology or cancer surveillance. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a computational method to reconstruct and visualize a 3D model of organs from an endoscopic video that captures the shape and surface appearance of the organ. A key aspect of our strategy is the use of advanced computer vision techniques and unmodified, clinical-grade endoscopy hardware with few constraints on the image acquisition protocol, which presents a low barrier to clinical translation. We validate the accuracy and robustness of our reconstruction and co-registration method using cystoscopy videos from tissue-mimicking bladder phantoms and show clinical utility during cystoscopy in the operating room for bladder cancer evaluation. As our method can powerfully augment the visual medical record of the appearance of internal organs, it is broadly applicable to endoscopy and represents a significant advance in cancer surveillance opportunities for big-data cancer research.