[Python] AOSLO Fast Image Mosaic

AOSLO Montage created from tens of tiny images
AOSLO Montage created from tens of tiny images

This repository contains python software pertaining to the registration and mosaicing of photoreceptor images as acquired by AOSLO. The software was developed as part of our 2018 Biomedical Optics Express publication, titled "Fast adaptive optics scanning light ophthalmoscope retinal montaging".

Github link: https://github.com/RViMLab/BOE2018-AOSLO-image-mosaic

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Fast adaptive optics scanning light ophthalmoscope retinal montaging,” Biomedical Optics Express, vol. 9, no. 9, pp.4317-4328, 2018. [pdf]

[Matlab] Plenoptic Camera Calibration

Setup for camera calibration
Setup for camera calibration

This repository contains matlab software pertaining to the checker-board-based geometric calibration of multi-focus plenoptic cameras, such as the ones from Raytrix. The software was developed as part of our 2017 Int. Conf. Computer Vision publication, titled "Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras".

Github link: https://github.com/RViMLab/ICCV2017-plenoptic-camera-calibration

S. Nousias, F. Chadebecq, J. Pichat, P. Keane, S. Ourselin, and C. Bergeles, “Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras,” Int. Conf. Computer vision, pp. 957 - 965 2017. [pdf]

[Python] MDRNNs for Photoreceptor Detection

Detection of photoreceptors on pathological retinas using MDRNNs.
Detection of photoreceptors on pathological retinas using MDRNNs.

This repository contains python software pertaining to the detection of photoreceptors in healthy and pathological retinal images, as acquired by AOSLO. The software was developed as part of our 2018 Scientific Reports publication, titled "Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning".

Github link: https://github.com/RViMLab/NSR2018-AOSLO-photoreceptor-detection

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning,” Scientific Reports, vol. 8, no.1, pp. 7911, 2018. [pdf]

[C++] Concentric tube robot - kinematics library

Example of multiple solved concentric tube robot configurations for a neurosurgical example.
Example of multiple solved concentric tube robot configurations for a neurosurgical example.

This repository contains C++ software pertaining to the calculation of the forward kinematics (torsional) of concentric tube robots in real-time. The software was developed as part of our 2017 IEEE Robotics and Automation Magazine publication, titled "Concentric tube robots: rapid, stable path-planning and guidance for surgical use".

Github link: https://github.com/RViMLab/RAM2017-CTR-kinematics

K. Leibrandt, C. Bergeles, and G.-Z. Yang. "Concentric Tube Robots: Rapid, Stable Path-Planning and Guidance for Surgical Use." IEEE Robotics & Automation Magazine 24.2 (2017): 42-53. [pdf]

[Matlab] Concentric tube robot design optimisation

Example of optimal 3-tube concentric tube robot for hydrocephalic ventricle access.
Example of optimal 3-tube concentric tube robot for hydrocephalic ventricle access.

This repository contains matlab software pertaining to the design of concentric tube robots based on task and anatomical constraints. The software was developed as part of our 2015 IEEE Trans. Robotics publication, titled "Concentric tube robot optimization based on task and anatomical constraints".

Github link: https://github.com/RViMLab/TRO2015-computational-robot-design

C. Bergeles, A. Gosline, N. V. Vasilyev, P. Codd, P. J. del Nido, and P. E. Dupont, “Concentric tube robot design and optimization based on task and anatomical constraints,” IEEE Trans. Robotics, vol. 31, no. 1, pp. 67–84, 2015. [pdf]

[Matlab] Unsupervised detection of cones in AOSLO

Example of detected photoreceptors in AOSLO images of healthy volunteers.
Example of detected photoreceptors in AOSLO images of healthy volunteers.

This repository contains matlab software pertaining to automated cone photoreceptor identification in adaptive optics scanning light ophthalmoscope (AOSLO) images. The software was developed as part of our 2017 Biomedical Optics Express publication, titled "Unsupervised Identification of Cone Photoreceptors in Non-Confocal Adaptive Optics Scanning Light Ophthalmoscope Images".

Github link: https://github.com/RViMLab/BOE2017-AOSLO-photoreceptor-detection

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, "Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomedical Optics Express, vol. 8, no. 6, pp. 4244–4251, 2017 [pdf].