Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation.

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data.

Assessment of mitral valve function in children and young adults with hypertrophic cardiomyopathy using three-dimensional echocardiography.

Objective: The objective of this study was to assess papillary muscle (PM) and mitral valve (MV) structure and function in children and young adults with mild and moderate hypertrophic cardiomyopathy (HCM) using real-time three-dimensional echocardiography (3DE) and to correlate them with HCM related adverse outcomes.

Methods: Transthoracic research 3DE was performed in HCM patients and controls matched for age and gender. Anterolateral and posteromedial PM mass, apical displacement of anterolateral PM, and left ventricular (LV) mass were measured and indexed to body surface area.

Cephalogram synthesis and landmark detection in dental cone-beam CT systems.

Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks.

Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research.

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas-chromatography mass-spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples.

Learning with Known Operators reduces Maximum Training Error Bounds.

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework.