Publicaties

Osteoporosis affects approximately 200 million people globally, resulting in 9 million fragility fractures annually. Vertebral fractures (VF) are the hallmark of the disease; the convey significant morbidity yet their identification lags. The growing number of abdomen and chest CT scans performand for unrelated indications provides an opportunity for the identification and reporting of VF. Convolutional Neural Networks (CNN) models have been ...

Depth estimation has always been a great challenge in the field of computer vision and machine learning. There is a rich literature focusing on depth estimation in stereo vision or in monocular imaging, while the domain of depth estimation in light field images is still in its infancy. The paper proposes a fully convolutional 3D neural network that estimates the disparity in light field images. The proposed method is parametric as it is able to ...

Objective. We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors. Approach. The required data is obtained by Monte Carlo simulation in GATE v8.2, based on a 50 x 50 x 16 mm(3) monolithic LYSO crystal coupled to an 8 x 8 readout array of silicon photomultipliers (SiPMs). The electronic signals are simulated as a sum of bi-exponentional functions centered around the ...

Objectives: To develop a visual ensemble approach using deep convolutional neural networks for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI. Methods: Multi-center 3D T1-DCE MRI (n=141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 ...

© 2019 Elsevier B.V. In this paper, we address the task of hyperspectral image classification using a 3-D Convolutional Neural Network (CNN). Instead of commonly used raw spectral features, discriminative features are obtained by factorizing the hyperspectral data with Non-negative Tensor Factorization (NTF). The factors consist of the endmembers’ spectral signature matrix as well as the abundance matrix. The acquired abundance maps are ...

The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic ...

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics ...