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Proceedings Paper

Motion induced segmentation of stone fragments in ureteroscopy video
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Paper Abstract

Ureteroscopy is a conventional procedure used for localization and removal of kidney stones. Laser is commonly used to fragment the stones until they are small enough to be removed. Often, the surgical team faces tremendous challenge to successfully perform this task, mainly due to poor image quality, presence of floating debris and occlusions in the endoscopy video. Automated localization and segmentation can help to perform stone fragmentation efficiently. However, the automatic segmentation of kidney stones is a complex and challenging procedure due to stone heterogeneity in terms of shape, size, texture, color and position. In addition, dynamic background, motion blur, local deformations, occlusions, varying illumination conditions and visual clutter from the stone debris make the segmentation task even more challenging. In this paper, we present a novel illumination invariant optical flow based segmentation technique. We introduce a multi-frame based dense optical flow estimation in a primal-dual optimization framework embedded with a robust data-term based on normalized correlation transform descriptors. The proposed technique leverages the motion fields between multiple frames reducing the effect of blur, deformations, occlusions and debris; and the proposed descriptor makes the method robust to illumination changes and dynamic background. Both qualitative and quantitative evaluations show the efficacy of the proposed method on ureteroscopy data. Our algorithm shows an improvement of 5-8% over all evaluation metrics as compared to the previous method. Our multi-frame strategy outperforms classically used two-frame model.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131514 (16 March 2020); doi: 10.1117/12.2549657
Show Author Affiliations
Soumya Gupta, Univ. of Oxford (United Kingdom)
Sharib Ali, Univ. of Oxford (United Kingdom)
Louise Goldsmith, Oxford Univ. Hospitals NHS Trust (United Kingdom)
Ben Turney, Oxford Univ. Hospitals NHS Trust (United Kingdom)
Jens Rittscher, Univ. of Oxford (United Kingdom)

Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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