Yongchao Xu

In IEEE Transactions on Image Processing

Abstract Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation.

Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iSeg-2017 challenge

By Li Wang, Dong Nie, Guannan Li, Élodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jiawei Chen, Kim-Han Thung, Toan Duc Bui, Jitae Shin, Guodong Zeng, Guoyan Zheng, Vladimir S. Fonov, Andrew Doyle, Yongchao Xu, Pim Moeskops, Josien P. W. Pluim, Christian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M. Benkarim, Adrià Casamitjana, Verónica Vilaplana, Weili Lin, Gang Li, Dinggang Shen

2019-04-11

In IEEE Transactions on Medical Imaging

Abstract Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging.

Standardized assessment of automatic segmentation of white matter hyperintensities: Results of the WMH segmentation challenge

By H. J. Kuijf, J. M. Biesbroek, J. de Bresser, R. Heinen, S. Andermatt, M. Bento, M. Berseth, M. Belyaev, M. J. Cardoso, A. Casamitjana, D. L. Collins, M. Dadar, A. Georgiou, M. Ghafoorian, D. Jin, A. Khademi, J. Knight, H. Li, X. Lladó, M. Luna, Q. Mahmood, R. McKinley, A. Mehrtash, S. Ourselin, B. Park, H. Park, S. H. Park, S. Pezold, Élodie Puybareau, L. Rittner, C. H. Sudre, S. Valverde, V. Vilaplana, R. Wiest, Yongchao Xu, Z. Xu, G. Zeng, J. Zhang, G. Zheng, C. Chen, W. van der Flier, F. Barkhof, M. A. Viergever, G. J. Biessels

2019-04-10

In IEEE Transactions on Medical Imaging

Abstract Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.

Left atrial segmentation in a few seconds using fully convolutional network and transfer learning

By Élodie Puybareau, Zhou Zhao, Younes Khoudli, Edwin Carlinet, Yongchao Xu, Jérôme Lacotte, Thierry Géraud

2018-10-25

In Proceedings of the workshop on statistical atlases and computational modelling of the heart (STACOM 2018), in conjunction with MICCAI

Abstract In this paper, we propose a fast automatic method that segments left atrial cavity from 3D GE-MRIs without any manual assistance, using a fully convolutional network (FCN) and transfer learning. This FCN is the base network of VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 Atrial Segmentation Challenge. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes.

Segmentation des hyperintensités de la matière blanche en quelques secondes à l’aide d’un réseau de neurones convolutif et de transfert d’apprentissage

By Élodie Puybareau, Yongchao Xu, Joseph Chazalon, Isabelle Bloch, Thierry Géraud

2018-05-04

In Actes du congrès reconnaissance des formes, image, apprentissage et perception (RFIAP), session spéciale “deep learning, deep in france”

Abstract Dans cet article, nous proposons une méthode automatique et rapide pour segmenter les hyper-intensités de la matière blanche (WMH) dans des images IRM cérébrales 3D, en utilisant un réseau de neurones entièrement convolutif (FCN) et du transfert d’apprentissage. Ce FCN est le réseau neuronal du Visual Geometry Group (VGG) pré-entraîné sur la base ImageNet pour la classification des images naturelles, et affiné avec l’ensemble des données d’entraînement du concours MICCAI WMH.

The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities

By Yongchao Xu, Baptiste Morel, Sonia Dahdouh, Élodie Puybareau, Alessio Virzì, Hélène Urien, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch

2018-03-22

In Medical Image Analysis

Abstract Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter.

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

By Yongchao Xu, Thierry Géraud, Élodie Puybareau, Isabelle Bloch, Joseph Chazalon

2018-02-06

In Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries— 3rd international workshop, BrainLes 2017, held in conjunction with MICCAI 2017, quebec city, QC, canada, september 14 2017, revised selected papers

Abstract

Segmentation d’IRM de cerveaux de nouveau-nés en quelques secondes à l’aide d’un réseau de neurones convolutif <i>pseudo-3D</i> et de transfert d’apprentissage

By Yongchao Xu, Thierry Géraud, Isabelle Bloch

2017-06-20

In Actes du 26e colloque GRETSI

Abstract L’imagerie par résonance magnétique (IRM) du cerveau est utilisée sur les nouveau-nés pour évaluer l’évolution du cerveau et diagnostiquer des maladies neurologiques. Ces examens nécessitent souvent une analyse quantitative des différents tissus du cerveau, de sorte qu’avoir une segmentation précise est essentiel. Dans cet article, nous proposons une méthode automatique rapide de segmentation en différents tissus des images IRM 3D de cerveaux de nouveau-nés ; elle utilise un réseau de neurones totalement convolutif (FCN) et du transfert d’apprentissage.

From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

By Yongchao Xu, Thierry Géraud, Isabelle Bloch

2017-06-12

In Proceedings of the 23rd IEEE international conference on image processing (ICIP)

Abstract Brain magnetic resonance imaging (MRI) is widely used to assess brain developments in neonates and to diagnose a wide range of neurological diseases in adults. Such studies are usually based on quantitative analysis of different brain tissues, so it is essential to be able to classify them accurately. In this paper, we propose a fast automatic method that segments 3D brain MR images into different tissues using fully convolutional network (FCN) and transfer learning.

La pseudo-distance du dahu

By Edwin Carlinet, Yongchao Xu, Nicolas Boutry, Thierry Géraud

2017-03-21

In Actes d’ORASIS

Abstract La distance de la barrière minimum est définie comme le plus petit intervalle de l’ensemble des niveaux de gris le long d’un chemin entre deux points dans une image. Pour cela, on considère que l’image est un graphe à valeurs sur les sommets. Cependant, cette définition ne correspond pas à l’interprétation d’une image comme étant une carte d’élévation, c’est-à-dire, un paysage continu d’une manière ou d’une autre. En se plaçant dans le cadre des fonctions multivoques, nous présentons une nouvelle définition pour cette distance.

Local intensity order transformation for robust curvilinear object segmentation

Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iSeg-2017 challenge

Standardized assessment of automatic segmentation of white matter hyperintensities: Results of the WMH segmentation challenge

Left atrial segmentation in a few seconds using fully convolutional network and transfer learning

Segmentation des hyperintensités de la matière blanche en quelques secondes à l’aide d’un réseau de neurones convolutif et de transfert d’apprentissage

The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

Segmentation d’IRM de cerveaux de nouveau-nés en quelques secondes à l’aide d’un réseau de neurones convolutif <i>pseudo-3D</i> et de transfert d’apprentissage

From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

La pseudo-distance du dahu

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