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7 papers · 2 filters
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…
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…
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…
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…
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…
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…