Publications associées à Learning Context Cues for Synapse Segmentation in EM Volumes

Publications associées (28)

Aggregating Spatial and Photometric Context for Photometric Stereo

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

A bistable inhibitory optoGPCR for multiplexed optogenetic control of neural circuits

Mauro Pulin

Information is transmitted between brain regions through the release of neurotransmitters from long-range projecting axons. Understanding how the activity of such long-range connections contributes to behavior requires efficient methods for reversibly mani ...

Nature Publishing Group2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

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

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

The multimodality cell segmentation challenge: toward universal solutions

Maxime Emmanuel Scheder, Jun Ma, Sahand Jamal Rahi, Xin Yang, Vojislav Gligorovski, Marco Labagnara

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Nature Publishing Group2024

Microsecond Time-Resolved Cryo-Electron Microscopy

Oliver Florian Harder

Recently, single-particle cryo-electron microscopy emerged as a technique capable of determining protein structures at near-atomic resolution and resolving protein dynamics with a temporal resolution ranging from second to milliseconds. This thesis describ ...

EPFL2024

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Veronica Lily Ravano

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

EPFL2024

Polydopamine-coated photoautotrophic bacteria for improving extracellular electron transfer in living photovoltaics

Ardemis Anoush Boghossian, Melania Reggente, Hanxuan Wang, Charlotte Elisabeth Marie Roullier, Mohammed Mouhib

Living photovoltaics are microbial electrochemical devices that use whole cell–electrode interactions to convert solar energy to electricity. The bottleneck in these technologies is the limited electron transfer between the microbe and the electrode surfac ...

2024

Structural study of nickelate based heterostructures

Duncan Thomas Lindsay Alexander, Bernat Mundet, Chih-Ying Hsu

Heterostructures consisting of SmNiO3 and NdNiO3 alternating layers with additional LaAlO3 spacer layers were grown and fully characterized by means of x-ray diffraction, atomic force microscopy, and scanning transmission electron microscopy. A change in t ...

Aip Publishing2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

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 ...

EPFL2024

The neural correlates of topographical disorientation-a lesion analysis study

Olaf Blanke, Lukas Heydrich, Eva Blondiaux

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Wiley2024

Principles of Network Plasticity in Neocortical Microcircuits

András Ecker

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

EPFL2023

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

Eilif Benjamin Muller, Michael Reimann, Marwan Muhammad Ahmed Abdellah, James Gonzalo King, András Ecker, Pramod Shivaji Kumbhar, Giuseppe Chindemi, Daniela Egas Santander, James Bryden Isbister, Ioannis Magkanaris, Sirio Bolaños Puchet, Jorge Blanco Alonso

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biologica ...

2024

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

Gulnaz Ganeeva

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 ...

EPFL2023

Engram cell connectivity as a mechanism for information encoding and memory function

Maurizio Ferdinando Pezzoli

Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

Cell Press2023

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Seyedbehzad Bozorgtabar, Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

GABAergic-like dopamine synapses in the brain

Maria Reva

Dopamine synapses play a crucial role in volitional movement and reward-related behaviors, while dysfunction of dopamine synapses causes various psychiatric and neurological disorders. Despite this significance, the true biological nature of dopamine synap ...

2023

Striatal Dopamine Signals and Reward Learning

Meriam Malekzadeh, Robin François Virginien Dard, Carl Petersen, Sylvain Crochet, Yanqi Liu, Parviz Ghaderi, Christos Sourmpis, Pol Bech Vilaseca, Mauro Pulin, Anthony Pierre Robert Renard

We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuro ...

Oxford Univ Press2023

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Li Zhang, Jiancheng Yang

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Springer International Publishing Ag2023