Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Computational Optical Sectioning in Fibre Bundle Endomicroscopy

The field of fibre bundle endomicroscopy has emerged to enable real-time imaging of cellular level features in-vivo. The gold standard is confocal laserscanning, enabling optical sectioning. Point-scan confocal suffers from lower speeds, a need for complex alignment, and the added cost of a laser. This thesis presents three developments in computational optical sectioning for fibre bundle endomicroscopy.The first development is in structured illumination (SIM) endomicroscopy. Lower-cost, simplified endomicroscopes have been developed which use widefield incoherent illumination. Optical sectioning can be introduced to these systems using SIM. SIM improves imaging using spatial modulation of the focal plane and capturing a three-frame sequence. The acquired images are then numerically processed to reject out-of-focus light. This thesis reports and characterises the first high-speed SIM endomicroscope built using a miniature array ofmirrors, a digital micromirror device. The second development is automated motion compensation in SIM endomicroscopy. As a multi frame process, SIM is susceptible to motion artefacts, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesise a larger effective field of view. I report and validate an automatic motion compensation technique to overcome motion artefacts and report the firstmosaics in SIM endomicroscopy.The third development is improvements in subtraction-based enhanced line scanning (ELS) endomicroscopy. The 2D scanning of a point scan confocal endomicroscope can be replaced by a scanning line which is synchronised to the sequential readout of a rolling shutter camera. While this leads to high-speed sectioning, as with all line scanning systems, far-from-focus light degrades images. It is possible to remove this by subtracting a second image taken with an offset detection slit. This has previously required two-cameras or two sequentialframes. The latter introduces motion artefacts. This thesis presents a novel approach to ELS using single frame acquisition with real-time mosaicking at 240frames/s

Improved Accuracy and Robustness of a Corneal Endothelial Cell Segmentation Method Based on Merging Superpixels

Clinical parameters related to the corneal endothelium can only be estimated by segmenting endothelial cell images. Specular microscopy is the current standard technique to image the endothelium, but its low SNR make the segmentation a complicated task. Recently, we proposed a method to segment such images by starting with an oversegmented image and merging the superpixels that constitute a cell. Here, we show how our merging method provides better results than optimizing the segmentation itself. Furthermore, our method can provide accurate results despite the degree of the initial oversegmentation, resulting into a precision and recall of 0.91 for the optimal oversegmentation.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Quantitative ImagingImPhys/Computational Imagin