Real-time Fiberscopic Image Improvement for Automated Lesion Detection in the Urinary Bladder
Eixelberger T, Weingärtner K, Maisch P, Bolenz C, Wittenberg T (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 25-30
Conference Proceedings Title: Informatik aktuell
Event location: Regensburg
ISBN: 9783658474218
DOI: 10.1007/978-3-658-47422-5_8
Abstract
Fiber endoscopes are flexible devices that use glass fibers for optical transmission of images out hollows such as the urinary bladder, trans-nasal cavities, or the lung. Even though bendable tip-chip endoscopes have been available for the past decades, fiberscopes are still in broad use, as they provide good images for a good price. However, images obtained from fiberscopes are particularly degraded by the honeycomb pattern related to the core and cladding of each fiber. To remove such honeycomb patterns for the human visual inspection of natural orifices as well as to condition such images to be used for machine and deep learning tasks, real-time compensation algorithms are needed. Using a large set of >15,000 fiberscopic images from the urinary bladder, two related approaches are investigated, namely (1) how to eliminate honeycomb patterns in real-time and hence improve the image quality, (2) to use such improved image data to train a deep-learning task to detect tumorous lesions in the urinary bladder. The investigated non-parametric filtering approach removes the honeycomb artifacts in the frequency domain using a DoG filter and thresholding. This filtering approach was evaluated on the fiberscopic images with and without visible honeycomb pattern using the BRISQUE image quality algorithm [1]. Secondly, the thus improved images were used as enhanced training dataset for a pre-trained deep neural networks (RT-DETR) for automated lesion detection in the urinary bladder. Based on the BRISQUE score, for approx. 10,000 cystoscopic images the fiber structures could be eliminated and the image quality could be improved. The BRISQUE score improved on average from 31.19 to 18.32. The F1-score for lesion detection using 572 cystoscopic improved from 0.623 to 0.802 using the honeycomb structured images for training, and to F1 = 0.830 with the images with the removed honeycomb structures. The investigated approach is on one hand capable to improve the fiberscopic honeycomb pattern in real-time, while on the other hand the honeycomb removal leads to improved rates for automated lesion detection in the urinary bladder.
Authors with CRIS profile
Thomas Eixelberger
Chair of Visual Computing (LGDV)
Thomas Wittenberg
Chair of Visual Computing (LGDV)
Involved external institutions
How to cite
APA:
Eixelberger, T., Weingärtner, K., Maisch, P., Bolenz, C., & Wittenberg, T. (2025). Real-time Fiberscopic Image Improvement for Automated Lesion Detection in the Urinary Bladder. In Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (Eds.), Informatik aktuell (pp. 25-30). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
MLA:
Eixelberger, Thomas, et al. "Real-time Fiberscopic Image Improvement for Automated Lesion Detection in the Urinary Bladder." Proceedings of the German Conference on Medical Image Computing, 2025, Regensburg Ed. Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2025. 25-30.
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