Hélène Urien

TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.

TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.

TL;DR: A new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for 18 F-FDG PET images, shows that the method detects all the lesions in the PET images.

TL;DR: A novel 3D method for multiple sclerosis segmentation on FLAIR Magnetic Resonance images (MRI), based on a lesion context-based criterion performed on a max-tree representation, which shows the ability of the method to detect almost all lesions.

TL;DR: A novel method for semi‐ automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max‐tree representations of the images is described, which responds to the increasing need for providing medical experts with semi‐automatic tools for image analysis, and overcomes the limitations of visual analysis alone.