LHU-VT: A Lightweight Hypercomplex U-Net with Vessel Thickness-Guided Dice Loss for retinal vessel segmentation.

Vision loss is often caused by retinal disorders, such as age-related macular degeneration and diabetic retinopathy, where early indicators like microaneurysms and hemorrhages appear as changes in retinal blood vessels. Accurate segmentation of these vessels in retinal images is essential for early diagnosis. However, retinal vessel segmentation presents challenges due to complex vessel structures, low contrast, and dense branching patterns, which are further complicated in resource-limited settings requiring lightweight solutions.

Intelligence against complexity: Machine learning for nonuniform temperature-field measurements through laser absorption.

The effect of spatial nonuniformity of the temperature distribution was examined on the capability of machine-learning algorithms to provide accurate temperature prediction based on Laser Absorption Spectroscopy. First, sixteen machine learning models were trained as surrogate models of conventional physical methods to measure temperature from uniform temperature distributions (uniform-profile spectra). The best three of them, Gaussian Process Regression (GPR), VGG13, and Boosted Random Forest (BRF) were shown to work excellently on uniform profiles but their performance degraded tremendously on nonuniform-profile spectra.

Towards a Virtual Fencing System: Training Domestic Sheep Using Audio Stimuli.

Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries.

Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation.

A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response.

Multi-Label Retinal Disease Classification Using Transformers.

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification.

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach.

Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors.

Realistic forward and inverse model mesh generation for rapid three-dimensional thoracic electrical impedance imaging.

One of the most promising clinical applications of Electrical Impedance Tomography (EIT) is real-time monitoring of lung function in ambulatory or ICU due to the rapid, non-invasive and non-ionizing nature of the measurements. However, to move this modality into routine clinical use will, as a minimum, require the development of realistic and computationally efficient forward and inverse meshes of the thorax and the lungs alongside mechanisms to extract quantitative information from the resulting reconstructed images. The latter will allow for reduction of artefacts and better localization of conductivity changes within the image domain.

A hybrid energy model for region based curve evolution - Application to CTA coronary segmentation.

Background And Objective: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation.

Patient-Specific Computational Models of Coronary Arteries Using Monoplane X-Ray Angiograms.

Coronary artery disease (CAD) is the most common type of heart disease in western countries. Early detection and diagnosis of CAD is quintessential to preventing mortality and subsequent complications. We believe hemodynamic data derived from patient-specific computational models could facilitate more accurate prediction of the risk of atherosclerosis.

Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images.

Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells.

Virtual fractional flow reserve by coronary computed tomography - hope or hype?

Studies evaluating the diagnostic performance of coronary computed tomography angiography (CTA) are consistent in demonstrating a high negative predictive accuracy, but only a modest positive predictive accuracy for the detection of significant coronary artery disease. Consequentially, there has been a considerable effort made to enhance the diagnostic capability of coronary CTA by developing scanner technology and also post-processing algorithms for coronary stenosis evaluation. Of these new developments, the proposition of being able to measure non-invasive fractional flow reserve by coronary computed tomography angiography (FFRct) has generated much recent interest.

Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts.

In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points.

On sparse forward solutions in non-stationary domains for the EIT imaging problem.

In the forward EIT-problem numerical solutions of an elliptic partial differential equation are required. Given the arbitrary geometries encountered, the Finite Element Method (FEM) is, naturally, the method of choice. Nowadays, in EIT applications, there is an increasing demand for finer Finite Element mesh models.

3-D quantitative vascular shape analysis for arterial bifurcations via dynamic tube fitting.

Reliable and reproducible estimation of vessel centerlines and reference surfaces is an important step for the assessment of luminal lesions. Conventional methods are commonly developed for quantitative analysis of the "straight" vessel segments and have limitations in defining the precise location of the centerline and the reference lumen surface for both the main vessel and the side branches in the vicinity of bifurcations. To address this, we propose the estimation of the centerline and the reference surface through the registration of an elliptical cross-sectional tube to the desired constituent vessel in each major bifurcation of the arterial tree.

A fisher information matrix interpretation of the NOSER algorithm in electrical impedance tomography.

In this paper, we employ the concept of the Fisher information matrix (FIM) to reformulate and improve on the "Newton's One-Step Error Reconstructor" (NOSER) algorithm. FIM is a systematic approach for incorporating statistical properties of noise, modeling errors and multi-frequency data. The method is discussed in a maximum likelihood estimator (MLE) setting.

Towards non-invasive EIT imaging of domains with deformable boundaries.

We investigate on the use of the Domain Embedding Method (DEM) for the forward modelling in EIT. This approach is suitably configured to overcome the model meshing bottleneck since it does not require that the mesh on the domain is adapted to the boundary surface. This is of crucial importance for, e.

An instance-based algorithm with auxiliary similarity information for the estimation of gait kinematics from wearable sensors.

Wearable human movement measurement systems are increasingly popular as a means of capturing human movement data in real-world situations. Previous work has attempted to estimate segment kinematics during walking from foot acceleration and angular velocity data. In this paper, we propose a novel neural network [GRNN with Auxiliary Similarity Information (GASI)] that estimates joint kinematics by taking account of proximity and gait trajectory slope information through adaptive weighting.

Generalized regression neural networks with multiple-bandwidth sharing and hybrid optimization.

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity.

A new method of three-dimensional coronary artery reconstruction from X-ray angiography: validation against a virtual phantom and multislice computed tomography.

Objective: To develop and implement a method for three-dimensional (3D) reconstruction of coronary arteries from conventional monoplane angiograms.

Background: 3D reconstruction of conventional coronary angiograms is a promising imaging modality for both diagnostic and interventional purposes.

Methods: Our method combines image enhancement, automatic edge detection, an iterative method to reconstruct the centerline of the artery and reconstruction of the diameter of the vessel by taking into consideration foreshortening effects.

Density-driven generalized regression neural networks (DD-GRNN) for function approximation.

This paper proposes a new nonparametric regression method, based on the combination of generalized regression neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic and substitutes the very large number of bandwidths with a much smaller number of trainable weights that control the regression model. It depends on sets of extracted data density features which reflect the density properties and distribution irregularities of the training data sets.

Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks.

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm.