Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Manjon, José V"'
Autor:
Morell-Ortega, Sergio, Ruiz-Perez, Marina, Gadea, Marien, Vivo-Hernando, Roberto, Rubio, Gregorio, Aparici, Fernando, de la Iglesia-Vaya, Maria, Catheline, Gwenaelle, Coupé, Pierrick, Manjón, José V.
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution ($1 \text{ mm}^{3}$) or using mono-modal data, the proposed method improves cereb
Externí odkaz:
http://arxiv.org/abs/2401.12074
The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task efficiently. However, the lack of annotated longitudinal data with new-a
Externí odkaz:
http://arxiv.org/abs/2206.08272
Autor:
Manjon, Jose V., Romero, Jose E., Vivo-Hernando, Roberto, Rubio, Gregorio, Aparici, Fernando, de la Iglesia-Vaya, Mariam, Coupe, Pierrick
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resources for both clinical and research environments. In the last years, this field has experienced many advances with successful techniques based on label fus
Externí odkaz:
http://arxiv.org/abs/2202.03920
Autor:
Kamraoui, Reda Abdellah, Ta, Vinh-Thong, Papadakis, Nicolas, Compaire, Fanny, Manjon, José V, Coupé, Pierrick
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks. In this work, we propose POPCORN, a novel
Externí odkaz:
http://arxiv.org/abs/2109.06361
Autor:
Kamraoui, Reda Abdellah, Ta, Vinh-Thong, Tourdias, Thomas, Mansencal, Boris, Manjon, José V, Coupé, Pierrick
Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation condit
Externí odkaz:
http://arxiv.org/abs/2012.07950
Publikováno v:
Physics in Medicine and Biology, 2020
Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious
Externí odkaz:
http://arxiv.org/abs/2005.06835
The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can sh
Externí odkaz:
http://arxiv.org/abs/2001.11789
Autor:
Manjón, José V., Romero, Jose E., Vivo-Hernando, Roberto, Rubio-Navarro, Gregorio, De la Iglesia-Vaya, María, Aparici-Robles, Fernando, Coupé, Pierrick
Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume is
Externí odkaz:
http://arxiv.org/abs/2001.05720
Autor:
Coupé, Pierrick, Mansencal, Boris, Clément, Michaël, Giraud, Rémi, de Senneville, Baudouin Denis, Ta, Vinh-Thong, Lepetit, Vincent, Manjon, José V.
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single
Externí odkaz:
http://arxiv.org/abs/1911.09098
Autor:
Manjon, Jose V., Coupe, Pierrick
This paper proposes a novel method for automatic MRI denoising that exploits last advances in deep learning feature regression and self-similarity properties of the MR images. The proposed method is a two-stage approach. In the first stage, an overco
Externí odkaz:
http://arxiv.org/abs/1911.04798