PurposeIn the current clinical standard of care, cystoscopic video is not routinely saved because it is cumbersome to review. Instead, clinicians rely on brief procedure notes and still frames to manage bladder pathology. Preserving discarded data via 3D reconstructions, which are convenient to review, has the potential to improve patient care. However, many clinical videos are collected by fiberscopes, which are lower cost but induce a pattern on frames that inhibit 3D reconstruction. The aim of our study is to remove the honeycomb-like pattern present in fiberscope-based cystoscopy videos to improve the quality of 3D bladder reconstructions.ApproachOur study introduces an algorithm that applies a notch filtering mask in the Fourier domain to remove the honeycomb-like pattern from clinical cystoscopy videos collected by fiberscope as a preprocessing step to 3D reconstruction. We produce 3D reconstructions with the video before and after removing the pattern, which we compare with a metric termed the area of reconstruction coverage (ARC), defined as the surface area (in pixels) of the reconstructed bladder. All statistical analyses use paired t-tests.ResultsPreprocessing using our method for pattern removal enabled reconstruction for all (n=5) cystoscopy videos included in the study and produced a statistically significant increase in bladder coverage (p=0.018).ConclusionsThis algorithm for pattern removal increases bladder coverage in 3D reconstructions and automates mask generation and application, which could aid implementation in time-starved clinical environments. The creation and use of 3D reconstructions can improve documentation of cystoscopic findings for future surgical navigation, thus improving patient treatment and outcomes.
White light cystoscopy is key to inform care of patients with suspected or confirmed bladder cancer. Although three-dimensional reconstructions of cystoscopy videos can facilitate rapid, comprehensive review, they are limited by the quality of the original video. Here we address a fundamental bottleneck to reconstruction quality: real-time assessment of frame quality for eventual clinician guidance. We implemented nine metrics and combined them with a random forest classifier that achieves a sensitivity and specificity of 90.6% and 93.7%, respectively. We will use this classifier to perform real-time clinician guidance to facilitate acquisition of high-quality cystoscopy videos that produce robust three-dimensional reconstructions.
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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