Expansile gas kinetics for pneumatic retinopexy.

PURPOSE: To study the behaviour of expansile intravitreal gases and air used in treating rhegmatogenous retinal detachment. METHODS: A validated mathematical model of gas expansion and absorption in human eyes was used to simulate the effect of varying volumes of pure air, SF6, C2F6 and C3F8 injected into the vitreous cavity. Variation in axial length was accounted for by using three different vitreous cavity volumes to represent hypermetropic, emmetropic and myopic eyes. RESULTS: The time course of varying volumes of pure air and fluorinated gases injected into the vitreous cavity were tabulated, with calculated parameters including volume of gas, percentage gas fills and corresponding retinal contact angles at different time points. CONCLUSION: We produced a comprehensive compilation of expansive gas kinetics aiming to facilitate surgeon selection of the most suitable choice of gas and volume to use, tailored to an individual patient's clinical need.

Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.

PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery. METHODS: Six thousand six hundred and sixty-one digital images of RRD treated by vitrectomy and internal tamponade were collected from the British and Eire Association of Vitreoretinal Surgeons database. Each image was classified as a primary surgical success or a primary surgical failure. The synthetic minority over-sampling technique was used to address class imbalance. We adopted the state-of-the-art deep convolutional neural network architecture Inception v3 to train, validate, and test deep learning models to predict the anatomical outcome of RRD surgery. The area under the curve (AUC), sensitivity, and specificity for predicting the outcome of RRD surgery was calculated for the best predictive deep learning model. RESULTS: The deep learning model was able to predict the anatomical outcome of RRD surgery with an AUC of 0.94, with a corresponding sensitivity of 73.3% and a specificity of 96%. CONCLUSION: A deep learning model is capable of accurately predicting the anatomical outcome of RRD surgery. This fully automated model has potential application in surgical care of patients with RRD.

Effect of Aqueous Dynamics on Gas Behavior Following Retinal Reattachment Surgery.

BACKGROUND AND OBJECTIVE: To determine how the gas concentration in air required to achieve full postoperative vitreous cavity fill varies in different aqueous outflow states. MATERIALS AND METHODS: A mathematical model was used to estimate gas dynamics. The change in gas bubble volume over time was calculated in an eye with normal aqueous outflow, ocular hypertension (OHT), and OHT with apraclonidine treatment. RESULTS: The concentration required was higher for all gases to achieve a full postoperative fill in OHT eyes versus normal eyes. Optimal gas concentrations were 22.6% for SF6, 13.9% for C2F6, and 11.6% for C3F8. Despite this, in OHT, the fill achieved was 95%, 95%, and 94% for SF6, C2F6, and C3F8, respectively. With apraclonidine, percentage fill improved for all gases. CONCLUSIONS: This is the first study to show aqueous outflow affects bubble size and indicates eyes with reduced outflow are at risk of underfill. This can ultimately affect surgical success. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:522-528.].

Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI.

Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient's treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa.

THEORETICAL GAS CONCENTRATIONS ACHIEVING 100% FILL OF THE VITREOUS CAVITY IN THE POSTOPERATIVE PERIOD: A Gas Eye Model Study.

PURPOSE: To determine the concentrations of different gas tamponades in air to achieve 100% fill of the vitreous cavity postoperatively and to examine the influence of eye volume on these concentrations. METHODS: A mathematical model of the mass transfer dynamics of tamponade and blood gases (O2, N2, and CO2) when injected into the eye was used. Mass transfer surface areas were calculated from published anatomical data. The model has been calibrated from published volumetric decay and composition results for three gases sulphahexafluoride (SF6), hexafluoroethane (C2F6), or perfluoropropane (C3F8). The concentrations of these gases (in air) required to achieve 100% fill of the vitreous cavity postoperatively without an intraocular pressure rise were determined. The concentrations were calculated for three volumes of the vitreous cavity to test whether ocular size influenced the results. RESULTS: A table of gas concentrations was produced. In a simulation of pars plana vitrectomy operations in which an 80% to 85% fill of the vitreous cavity with gas was achieved at surgery, the concentrations of the 3 gases in air to achieve 100% fill postoperatively were 10% to 13% for C3F8, 12% to 15% for C2F6, and 19% to 25% for SF6. These were similar to the so-called "nonexpansive" concentrations used in the clinical setting. The calculations were repeated for three different sizes of eye. Aiming for an 80% fill at surgery and 100% postoperatively, an eye with a 4-mL vitreous cavity required 24% SF6, 15% C2F6, or 13% C3F8; 7.2 mL required 25% SF6, 15% C2F6, or 13% C3F8; and 10 mL required 25% SF6, 16% C2F6, or 13% C3F8. When using 100% gas (e.g., used in pneumatic retinopexy), to achieve 100% fill postoperatively, the minimum vitreous cavity fill at surgery was 43% for SF6, 29% for C2F6, and 25% for C3F8 and was only minimally changed by variation in the size of the eye. CONCLUSION: A table has been produced, which could be used for surgical innovation in gas usage in the vitreous cavity. It provides concentrations for different percentage fills, which will achieve a moment postoperatively with a full fill of the cavity without a pressure rise. Variation in axial length and size of the eye does not seem to alter the values in the table significantly. Those using pneumatic retinopexy need to increase the volume of gas injected with increased size of the eye to match the percentage fill of the vitreous cavity recommended for a given tamponade agent.

Bayesian Helmholtz Stereopsis with Integrability Prior.

Helmholtz Stereopsis is a 3D reconstruction method uniquely independent of surface reflectance. Yet, its sub-optimal maximum likelihood formulation with drift-prone normal integration limits performance. Via three contributions this paper presents a complete novel pipeline for Helmholtz Stereopsis. First, we propose a Bayesian formulation replacing the maximum likelihood problem by a maximum a posteriori one. Second, a tailored prior enforcing consistency between depth and normal estimates via a novel metric related to optimal surface integrability is proposed. Third, explicit surface integration is eliminated by taking advantage of the accuracy of prior and high resolution of the coarse-to-fine approach. The pipeline is validated quantitatively and qualitatively against alternative formulations, reaching sub-millimetre accuracy and coping with complex geometry and reflectance.

Colour Helmholtz Stereopsis for Reconstruction of Dynamic Scenes with Arbitrary Unknown Reflectance.

Helmholtz Stereopsis is a powerful technique for reconstruction of scenes with arbitrary reflectance properties. However, previous formulations have been limited to static objects due to the requirement to sequentially capture reciprocal image pairs (i.e. two images with the camera and light source positions mutually interchanged). In this paper, we propose colour Helmholtz Stereopsis-a novel framework for Helmholtz Stereopsis based on wavelength multiplexing. To address the new set of challenges introduced by multispectral data acquisition, the proposed Colour Helmholtz Stereopsis pipeline uniquely combines a tailored photometric calibration for multiple camera/light source pairs, a novel procedure for spatio-temporal surface chromaticity calibration and a state-of-the-art Bayesian formulation necessary for accurate reconstruction from a minimal number of reciprocal pairs. In this framework, reflectance is spatially unconstrained both in terms of its chromaticity and the directional component dependent on the illumination incidence and viewing angles. The proposed approach for the first time enables modelling of dynamic scenes with arbitrary unknown and spatially varying reflectance using a practical acquisition set-up consisting of a small number of cameras and light sources. Experimental results demonstrate the accuracy and flexibility of the technique on a variety of static and dynamic scenes with arbitrary unknown BRDF and chromaticity ranging from uniform to arbitrary and spatially varying.

Interactive animation of 4D performance capture.

A 4D parametric motion graph representation is presented for interactive animation from actor performance capture in a multiple camera studio. The representation is based on a 4D model database of temporally aligned mesh sequence reconstructions for multiple motions. High-level movement controls such as speed and direction are achieved by blending multiple mesh sequences of related motions. A real-time mesh sequence blending approach is introduced, which combines the realistic deformation of previous nonlinear solutions with efficient online computation. Transitions between different parametric motion spaces are evaluated in real time based on surface shape and motion similarity. Four-dimensional parametric motion graphs allow real-time interactive character animation while preserving the natural dynamics of the captured performance.

Using points at infinity for parameter decoupling in camera calibration.

The majority of camera calibration methods, including the Gold Standard algorithm, use point-based information and simultaneously estimate all calibration parameters. In contrast, we propose a novel calibration method that exploits line orientation information and decouples the problem into two simpler stages. We formulate the problem as minimization of the lateral displacement between single projected image lines and their vanishing points. Unlike previous vanishing point methods, parallel line pairs are not required. Additionally, the invariance properties of vanishing points mean that multiple images related by pure translation can be used to increase the calibration data set size without increasing the number of estimated parameters. We compare this method with vanishing point methods and the Gold Standard algorithm and demonstrate that it has comparable performance.