相关论文: End-to-end semantic segmentation of personalized d…
Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols…
The reconstruction of brain neural network connections occurs not only during the infancy and early childhood stages of brain development, but also in patients with cognitive impairment in middle and old age under the therapy with…
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but…
Brain tissue segmentation has demonstrated great utility in quantifying MRI data through Voxel-Based Morphometry and highlighting subtle structural changes associated with various conditions within the brain. However, manual segmentation is…
Advancements in neurosurgical robotics have improved medical procedures, particularly deep brain stimulation, where robots combine human and machine intelligence to precisely implant electrodes in the brain. While effective, this procedure…
Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of Gliobastoma Multiform (GBM) and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of…
Electrical brain stimulation is currently being investigated as a therapy for neurological disease. However, opportunities to optimize such therapies are challenged by the fact that the beneficial impact of focal stimulation on both…
The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current…
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)…
Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and is approved for a range of diagnostic and…
Electrical impedance tomography (EIT) is a promising technique for biomedical imaging. The strength of EIT is its ability to reconstruct images of the body's internal structures through radiation-safe techniques. EIT is regarded as safe for…
Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the…
The main objective of the electrical stimulation of the brain is to generate action potentials from the application of electromagnetic fields. Among the available techniques, transcranial electrical stimulation (TES) represents a popular…
Background: Finite element method (FEM) simulations of the electric field magnitude (EF) are commonly used to estimate the affected tissue surrounding the active contact of deep brain stimulation (DBS) leads. Previous studies have found…
In this work, we explore the various Brain Neuron tracking techniques, which is one of the most significant applications of Diffusion Tensor Imaging. Tractography provides us with a non-invasive method to analyze underlying tissue…
We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental health diseases. Our solution targets the current challenges…
Deep Brain Stimulation (DBS) is a well-established neurosurgical treatment aiming at symptom alleviation in a range of neurological and psychiatric diseases. Computational models of DBS are widely used to investigate the effects of…
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current…
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be…
Background: Spinal cord injury (SCI) rehabilitation remains a major clinical challenge, with limited treatment options for functional recovery. Temporal interference (TI) electrical stimulation has emerged as a promising non-invasive…