iOCT-guided simulated subretinal injections: a comparison between manual and robot-assisted techniques in an ex-vivo porcine model.

The purpose of this study is to compare robot-assisted and manual subretinal injections in terms of successful subretinal blistering, reflux incidences and damage of the retinal pigment epithelium (RPE). Subretinal injection was simulated on 84 ex-vivo porcine eyes with half of the interventions being carried out manually and the other half by controlling a custom-built robot in a master-slave fashion. After pars plana vitrectomy (PPV), the retinal target spot was determined under a LUMERA 700 microscope with microscope-integrated intraoperative optical coherence tomography (iOCT) RESCAN 700 (Carl Zeiss Meditec, Germany).

Comparison of Robot-Assisted and Manual Cannula Insertion in Simulated Big-Bubble Deep Anterior Lamellar Keratoplasty.

This study aimed to compare the efficacy of robot-assisted and manual cannula insertion in simulated big-bubble deep anterior lamellar keratoplasty (DALK). Novice surgeons with no prior experience in performing DALK were trained to perform the procedure using manual or robot-assisted techniques. The results showed that both methods could generate an airtight tunnel in the porcine cornea, and result in successful generation of a deep stromal demarcation plane representing sufficient depth reached for big-bubble generation in most cases.

Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT.

Intraoperative Optical Coherence Tomography (iOCT) has advanced in recent years to provide real-time high resolution volumetric imaging for ophthalmic surgery. It enables real-time 3D feedback during precise surgical maneuvers. Intraoperative 4D OCT generally exhibits lower signal-to-noise ratio compared to diagnostic OCT and visualization is complicated by instrument shadows occluding retinal tissue.

Machine Learning Techniques for Ophthalmic Data Processing: A Review.

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma.

Automatic intraoperative optical coherence tomography positioning.

Purpose: Intraoperative optical coherence tomography (iOCT) was recently introduced as a new modality for ophthalmic surgeries. It provides real-time cross-sectional information at a very high resolution. However, properly positioning the scan location during surgery is cumbersome and time-consuming, as a surgeon needs both his hands for surgery.

Fast 5DOF needle tracking in iOCT.

Purpose: Intraoperative optical coherence tomography (iOCT) is an increasingly available imaging technique for ophthalmic microsurgery that provides high-resolution cross-sectional information of the surgical scene. We propose to build on its desirable qualities and present a method for tracking the orientation and location of a surgical needle. Thereby, we enable the direct analysis of instrument-tissue interaction directly in OCT space without complex multimodal calibration that would be required with traditional instrument tracking methods.

SonifEye: Sonification of Visual Information Using Physical Modeling Sound Synthesis.

Sonic interaction as a technique for conveying information has advantages over conventional visual augmented reality methods specially when augmenting the visual field with extra information brings distraction. Sonification of knowledge extracted by applying computational methods to sensory data is a well-established concept. However, some aspects of sonic interaction design such as aesthetics, the cognitive effort required for perceiving information, and avoiding alarm fatigue are not well studied in literature.

CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery.

Detection of instrument tip in retinal microsurgery videos is extremely challenging due to rapid motion, illumination changes, the cluttered background, and the deformable shape of the instrument. For the same reason, frequent failures in tracking add the overhead of reinitialization of the tracking. In this work, a new method is proposed to localize not only the instrument center point but also its tips and orientation without the need of manual reinitialization.

Real-time localization of articulated surgical instruments in retinal microsurgery.

Real-time visual tracking of a surgical instrument holds great potential for improving the outcome of retinal microsurgery by enabling new possibilities for computer-aided techniques such as augmented reality and automatic assessment of instrument manipulation. Due to high magnification and illumination variations, retinal microsurgery images usually entail a high level of noise and appearance changes. As a result, real-time tracking of the surgical instrument remains challenging in in-vivo sequences.

Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.

Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications.

Hunting for necrosis in the shadows of intravascular ultrasound.

Coronary artery disease leads to failure of coronary circulation secondary to accumulation of atherosclerotic plaques. In adjunction to primary imaging of such vascular plaques using coronary angiography or alternatively magnetic resonance imaging, intravascular ultrasound (IVUS) is used predominantly for diagnosis and reporting of their vulnerability. In addition to plaque burden estimation, necrosis detection is an important aspect in reporting of IVUS.

Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI.

In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left ventricles in cardiac Cine MRI sequences. We introduce a new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly. In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle.

Comparison of kinematics of ACL-deficient and healthy knees during passive flexion and isometric leg press.

Background: Studying the kinematics of the ACL deficient (ACLD) knees, during different physiological activities and muscle contraction patterns, can improve our understanding of the joint's altered biomechanics due to ACL deficiency as well as the efficacy and safety of the rehabilitations exercises.

Methods: Twenty-five male volunteers, including 11 normal and 14 unilateral ACLD subjects, participated in this study. The kinematics of the injured knees of the ACLD subjects was compared with their intact knees and the healthy group during passive flexion and isometric leg press with the knees flexed from full extension to 45° flexion, with 15° intervals.

Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology.

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling.

Manifold learning for shape guided segmentation of cardiac boundaries: application to 3D+t cardiac MRI.

In this paper we propose a new method for shape guided segmentation of cardiac boundaries based on manifold learning of the shapes represented by the phase field approximation of the Mumford-Shah functional. A novel distance is defined to measure the similarity of shapes without requiring deformable registration. Cardiac motion is compensated and phases are mapped into one reference phase, that is the end of diastole, to avoid time warping and synchronization at all cardiac phases.

Joint edge detection and motion estimation of cardiac MR image sequence by a phase field method.

In this paper a variational framework for joint segmentation and motion estimation is employed for inspecting heart in Cine MRI sequences. A functional including Mumford-Shah segmentation and optical flow based dense motion estimation is approximated using the phase-field technique. The minimizer of the functional provides an optimum motion field and edge set by considering both spatial and temporal discontinuities.