output
20062025
most citedNon-invasive real-time imaging through scattering layers and around corners via speckle correlations

1.2k citations

Showing 2024 · eess.IVShow all

7 papers · 2 filters

eess.IV20245 cited

Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation

Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +3

The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in c…

eess.IV2024

Deep vessel segmentation with joint multi-prior encoding

Amine Sadikine, Bogdan Badic, Enzo Ferrante +4

The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated…

eess.IV20241 cited

Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +4

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this res…

eess.IV20248 cited

A plug-and-play framework for curvilinear structure segmentation based on a learned reconnecting regularization

Sophie Carneiro-Esteves, Antoine Vacavant, Odyssée Merveille

Curvilinear structures are present in various fields in image processing such as blood vessels in medical imaging or roads in remote sensing. Their detection is crucial for many ap…

eess.IV20242 cited

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

Sophie Loizillon, Simona Bottani, Stéphane Mabille +6

The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs…

eess.IV20248 cited

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

Ravi Hassanaly, Camille Brianceau, Maëlys Solal +2

Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel…