Blue light cystoscopy (BLC) and white light cystoscopy (WLC) are standard of care tools to image the bladder for suspicious areas of tumor development. Having clear, high-quality frames in cystoscopy videos are crucial to sensitive, efficient detection of bladder tumors. Vessel features carry rich information but are often lost or poorly visualized in frames containing illumination artifacts or impacted by impurities in the bladder. In our study, we introduced an automatic WLC and BLC classification method for cystoscopy video analysis and proposed an image enhancement pipeline that addresses the loss of features for cystoscopy videos containing WLC and BLC frames.
Blue light cystoscopy (BLC) has been demonstrated to detect bladder tumors with better sensitivity than white light cystoscopy (WLC); however, the use of BLC is limited to the operating room. In this study, we aim to bring BLC to the clinic by transforming WLC frames into digitally-stained BLC-like frames. We collected region-matched WLC and BLC videos from TURBT procedures and generated BLC-like frames, using WLC frames as input and the matched BLC frames as target. We will discuss the staining performances with perfectly registered WLC-BLC datasets, as well as WLC and BLC video clips collected with commercial clinical systems.
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