Isabelle Bloch

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract This paper aims at providing an overview of the use of mathematical morphology, in its algebraic setting, in several fields of artificial intelligence (AI). Three domains of AI will be covered. In the first domain, mathematical morphology operators will be expressed in some logics (propositional, modal, description logics) to answer typical questions in knowledge representation and reasoning, such as revision, fusion, explanatory relations, satisfying usual postulates. In the second domain, spatial reasoning will benefit from spatial relations modeled using fuzzy sets and morphological operators, with applications in model-based image understanding.

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.

A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI

By Baptiste Morel, Yongchao Xu, Alessio Virzi, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch

2016-05-20

In Proceedings of the annual international conference of the IEEE engineering in medicine and biology society

Abstract The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation.

Local reasoning in fuzzy attribute graphs for optimizing sequential segmentation

By Geoffroy Fouquier, Jamal Atif, Isabelle Bloch

2007-02-15

In Proceedings of the 6th IAPR TC-15 workshop on graph-based representations in pattern recognition (GBR)

Abstract Spatial relations play a crucial role in model-based image recognition and interpretation due to their stability compared to many other image appearance characteristics. Graphs are well adapted to represent such information. Sequential methods for knowledge-based recognition of structures require to define in which order the structures have to be recognized. We propose to address this problem of order definition by developing algorithms that automatically deduce sequential segmentation paths from fuzzy spatial attribute graphs.

Spatial reasoning with relative incomplete information on relative positioning

By Réda Dehak, Isabelle Bloch, Henri Maı̂tre

2005-09-01

In IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract This paper describes a probabilistic method of inferring the position of a point with respect to a reference point knowing their relative spatial position to a third point. We address this problem in the case of incomplete information where only the angular spatial relationships are known. The use of probabilistic representations allows us to model prior knowledge. We derive exact formulae expressing the conditional probability of the position given the two known angles, in typical cases: uniform or Gaussian random prior distributions within rectangular or circular regions.

Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI

By Isabelle Bloch, Olivier Colliot, Oscar Camara, Thierry Géraud

2004-07-09

In Pattern Recognition Letters

Abstract Spatial relations play an important role in recognition of structures embedded in a complex environment and for reasoning under imprecision. Several types of relationships can be modeled in a unified way using fuzzy mathematical morphology. Their combination benefits from the powerful framework of fuzzy set theory for fusion tasks and decision making. This paper presents several methods of fusion of information about spatial relationships and illustrates them on the example of model-based recognition of brain structures in 3D magnetic resonance imaging.

On some associations between mathematical morphology and artificial intelligence

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

A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI

Local reasoning in fuzzy attribute graphs for optimizing sequential segmentation

Spatial reasoning with relative incomplete information on relative positioning

Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI

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