Publications associées à Rendu volumique direct

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Martin Weigert

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Nature Portfolio2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

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 ...

EPFL2024

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Xiang Li, Amos Sironi, Jian Zhou, Jie Zhou, Siqi Liu

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

2023

Three-dimensional micro-CT images of UV-frozen flow fronts recorded at different flow regimes

Véronique Michaud, Jacobus Gerardus Rudolph Staal

Segmented micro-CT images of flow fronts produced by UV-flow freezing. Front morphologies correspond to capilary-dominated, equilibrated and viscous-dominated flow regimes. ...

Zenodo2023

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

Sub-micrometer morphology of human atherosclerotic plaque revealed by synchrotron radiation-based mu CT-A comparison with histology

Goran Lovric, Anne Bonnin

Histology is a long standing and well-established gold standard for pathological characterizations. In recent years however, synchrotron radiation-based micro-computed tomography (SR mu CT) has become a tool for extending the imaging of two-dimensional thi ...

PUBLIC LIBRARY SCIENCE2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Patrick Moller Jensen, Mian Akbar Shah

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

PMLR2022

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

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 ...

EPFL2022