Clinical predictors in acute optic neuritis: Analysis based on clinical trial data.

Purpose: To identify baseline clinical predictors of visual outcomes six months after acute optic neuritis using data from our completed clinical neuroprotection trial (TONE study).

Design: Secondary analysis of data from the TONE study cohort (NCT01962571).

Subjects: Total of 103 patients presenting within 10 days of a first episode of acute unilateral optic neuritis as a clinically isolated syndrome with baseline high contrast visual acuity (HCVA) < 20/40 Snellen (logMAR 0.

A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging.

The release of Ca ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classifying associated local Ca release events is particularly important, as these events provide insight into the mechanisms, interplay, and interdependencies of local Carelease events underlying global intracellular Casignaling. However, time-consuming and labor-intensive procedures often complicate analysis, especially with low signal-to-noise ratio imaging data.

Disease Course of Clinically Isolated Optic Neuritis.

Background And Objectives: Optic neuritis is the most common optic neuropathy in young adults and a frequent manifestation of multiple sclerosis. Its clinical course is pertinent to the design of visual pathway neuroprotection trials.

Methods: This is a secondary analysis of longitudinal data from the TONE trial, which included 103 patients from 12 German academic tertiary centers with acute unilateral optic neuritis as a clinically isolated syndrome and baseline high-contrast visual acuity <0.

Deep Learning-Based Automated Detection of Retinal Breaks and Detachments on Fundus Photography.

Purpose: The purpose of this study was to develop a deep learning algorithm, to detect retinal breaks and retinal detachments on ultra-widefield fundus (UWF) optos images using artificial intelligence (AI).

Methods: Optomap UWF images of the database were annotated to four groups by two retina specialists: (1) retinal breaks without detachment, (2) retinal breaks with retinal detachment, (3) retinal detachment without visible retinal breaks, and (4) a combination of groups 1 to 3. The fundus image data set was split into a training set and an independent test set following an 80% to 20% ratio.

Müller matrix polarimetry for pancreatic tissue characterization.

Polarimetry is an optical characterization technique capable of analyzing the polarization state of light reflected by materials and biological samples. In this study, we investigate the potential of Müller matrix polarimetry (MMP) to analyze fresh pancreatic tissue samples. Due to its highly heterogeneous appearance, pancreatic tissue type differentiation is a complex task.

Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery.

Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.

Methods: This work investigates the feasibility of using an OoD detector to identify when images from the iiOCT probe are inappropriate for subsequent machine learning-based distance estimation.

Mask then classify: multi-instance segmentation for surgical instruments.

Purpose: The detection and segmentation of surgical instruments has been a vital step for many applications in minimally invasive surgical robotics. Previously, the problem was tackled from a semantic segmentation perspective, yet these methods fail to provide good segmentation maps of instrument types and do not contain any information on the instance affiliation of each pixel. We propose to overcome this limitation by using a novel instance segmentation method which first masks instruments and then classifies them into their respective type.

Assessment of patient specific information in the wild on fundus photography and optical coherence tomography.

In this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient's age and sex from fundus images, OCT cross sections and OCT volumes.

Simulation of hyperelastic materials in real-time using deep learning.

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: A data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.

Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans.

In ophthalmology, retinal biological markers, or biomarkers, play a critical role in the management of chronic eye conditions and in the development of new therapeutics. While many imaging technologies used today can visualize these, Optical Coherence Tomography (OCT) is often the tool of choice due to its ability to image retinal structures in three dimensions at micrometer resolution. But with widespread use in clinical routine, and growing prevalence in chronic retinal conditions, the quantity of scans acquired worldwide is surpassing the capacity of retinal specialists to inspect these in meaningful ways.

Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. This sequential process then yields a 2D visual field image that is critical for clinical use.

A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain.

Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods.

Learning structured models for segmentation of 2-D and 3-D imagery.

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal performance. A popular choice is to learn them using a large-margin framework and more specifically structured support vector machines (SSVM).

Exploiting enclosing membranes and contextual cues for mitochondria segmentation.

In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.

A morphological approach to curvature-based evolution of curves and surfaces.

We introduce new results connecting differential and morphological operators that provide a formal and theoretically grounded approach for stable and fast contour evolution. Contour evolution algorithms have been extensively used for boundary detection and tracking in computer vision. The standard solution based on partial differential equations and level-sets requires the use of numerical methods of integration that are costly computationally and may have stability issues.