Prof. Jens Rittscher Profile

Prof. Jens Rittscher

at Univ of Oxford

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Proceedings Article | 16 March 2020 Paper

Motion induced segmentation of stone fragments in ureteroscopy video

Soumya Gupta, Sharib Ali, Louise Goldsmith, Ben Turney, Jens Rittscher

Proceedings Volume 11315, 1131514 (2020) https://doi.org/10.1117/12.2549657

KEYWORDS: Endoscopy, Image segmentation, Optical flow, Renal calculi, Video, Image quality, Motion models, Kidney

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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.

Proceedings Article | 23 February 2018 Paper

Sensorless adaptive optics for isoSTED nanoscopy

Jacopo Antonello, Xiang Hao, Edward Allgeyer, Joerg Bewersdorf, Jens Rittscher, Martin Booth

Proceedings Volume 10502, 1050206 (2018) https://doi.org/10.1117/12.2288973

KEYWORDS: Image quality, Microscopy, Aberration correction, Wavelets, Adaptive optics, Microscopes, Super resolution, Stimulated emission depletion microscopy, Sensors, Super resolution microscopy

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The presence of aberrations is a major concern when using fluorescence microscopy to image deep inside tissue. Aberrations due to refractive index mismatch and heterogeneity of the specimen under investigation cause severe reduction in the amount of fluorescence emission that is collected by the microscope. Furthermore, aberrations adversely affect the resolution, leading to loss of fine detail in the acquired images. These phenomena are particularly troublesome for super-resolution microscopy techniques such as isotropic stimulated-emission-depletion microscopy (isoSTED), which relies on accurate control of the shape and co-alignment of multiple excitation and depletion foci to operate as expected and to achieve the super-resolution effect.

Aberrations can be suppressed by implementing sensorless adaptive optics techniques, whereby aberration correction is achieved by maximising a certain image quality metric. In confocal microscopy for example, one can employ the total image brightness as an image quality metric. Aberration correction is subsequently achieved by iteratively changing the settings of a wavefront corrector device until the metric is maximised. This simplistic approach has limited applicability to isoSTED microscopy where, due to the complex interplay between the excitation and depletion foci, maximising the total image brightness can lead to introducing aberrations in the depletion foci. In this work we first consider the effects that different aberration modes have on isoSTED microscopes. We then propose an iterative, wavelet-based aberration correction algorithm and evaluate its benefits.

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