Deep Learning for Post-Contrast T1-Weighted Brain MRI Synthesis

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases...

Algal Bloom Forecasting: Classical Machine Learning versus Deep-Learning

The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the...

Automated rooftop solar panel detection through Convolutional Neural Networks

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient...

Semantic segmentation of historical photographs of the Antarctica peninsula

A huge archive of historical images of the Antarctica taken by the US Navy between 1940 and 2000 is publicly available. These images have not yet been used for large-scale computer-driven analysis as they were captured with analog cameras. They were only later digitized and contain no semantic information. Most modern deep-learning based...

Surrogate convolutional neural network models for steady computational fluid dynamics simulations

A convolution neural network (CNN)-based approach for the construction of reduced order surrogate models for computational fluid dynamics (CFD) simulations is introduced; it is inspired by the approach of Guo, Li, and Iori [X. Guo, W. Li, and F. Iorio, Convolutional neural networks for steady flow approximation, in Proceedings of the 22nd ACM...

Landslide detection and mapping on Synthetic Aperture Radar amplitude satellite imagery

There is a growing demand for detailed and accurate landslide maps and inventories worldwide. The mapping of landslides is essential for emergency response, disaster mitigation and a better understanding of landslides. Currently, it is still difficult to detect the timing and extent of landslides accurately through satellite imagery. Most often...

Exploring deep learning to improve allelic peak calling in forensic DNA analysis

When processing a trace DNA sample at the Netherlands Forensic Institute, an STR electropherogram can be created. An analyst uses this electropherogram and analysis software to read out peaks signifying DNA. After analysis, the DNA profile is used in the interpretation process, which can include the comparison to a reference DNA profile of a...

Automated segmentation of lacunes of presumed vascular origin in brain MRI scans

Lacunes of presumed vascular origin (lacunes) are small lesions in the brain and are an important indicator of cerebral small vessel disease (cSVD). To gain more insight in this disease, obtaining more information about the shape, size and location of lacunes is essential. However, manual segmentation (the voxel-wise labeling of lacunes in a...

Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation

Background<br/><br/>Corneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic...

Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks

In images of the corneal endothelium (CE) acquired by specular microscopy, endothelial cells are commonly only visible in a part of the image due to varying contrast, mainly caused by challenging imaging conditions as a result of a strongly curved endothelium. In order to estimate the morphometric parameters of the corneal endothelium, the...