Fibertract segmentation in position orientation space from high angular resolution diffusion MRI

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Over the last two decades, Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) has been developed as a powerful MR imaging modality that allows estimating non-invasively the microscopic structure of biological tissues by exploiting the natural motion of ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage pri ...

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ a ...

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system and affects almost 3 million people worldwide. There is currently no cure for MS, and its symptoms, starting with fatigue and weakness, often progress over time ...

As technology continues to evolve, robots are becoming an intrinsic part of our lives. While robots surpass human capabilities in precision and speed, they are far from matching humans' ability to adapt to unexpected changes. Humans can quickly and complia ...

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

Background: Mapping diffusion MRI tractography streamlines to the cortical surface facilitates the integration of white matter features onto gray matter, especially for connectivity analysis. ...

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the ...

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rel ...

Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices ...

The cameras are invented by imitating the human visual system to capture the scene. The camera technologies have been substantially advanced in recent years. 108 MP resolution with 100x hybrid zoom has become standard features for smartphone flagships. In ...

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, how ...

Fibertract segmentation in position orientation space from high angular resolution diffusion MRI

Chat with Graph Search

Simulation-based Microstructure Imaging: Towards Realistic White Matter Modeling using Monte Carlo Methods in DW-MRI

Aggregating Spatial and Photometric Context for Photometric Stereo

Towards Trustworthy Deep Learning for Image Reconstruction

Detecting Anomalies and Obstacles in Road Scenes

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

Advancing the Adaptability of Compliant Robot Controllers for Exploration, Interaction, and Manipulation

Object Priors for Volumetric Image Segmentation

Improving the Training of Compact Neural Networks for Visual Recognition

Human-Centered Scene Understanding via Crowd Counting

Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis

Differentiable Patch Selection for Image Recognition

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

Loss landscape and symmetries in Neural Networks

Learning stereo reconstruction with deep neural networks

Multi-View Triangulation: Systematic Comparison and an Improved Method

Beyond Cameras: Real-Time High-Resolution 3D and Panoramic MultiView Vision Systems with Their Applications

Single-Stage 6D Object Pose Estimation