Segmentation of the cortical plate in fetal brain MRI with a topological loss

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. In this paper, we propose for the first time the integration of a topological constraint, as an additional loss function, to enhance the morphological consistency of a deep learning-based segmentation of the fetal cortical plate. We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline method. Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Interactive segmentation based on component-trees

International audienceComponent-trees associate to a discrete grey-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an original interactive segmen- tation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the grey-level structure of the image) a given binary target selected beforehand in the image. A proof of the algorithmic efficiency of this methodological scheme is proposed. Concrete application examples on magnetic resonance imaging (MRI) data emphasise its actual computational efficiency and its usefulness for interactive segmentation of real images

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation

MR Imaging of Adverse Effects and Ocular Growth Decline after Selective Intra-Arterial Chemotherapy for Retinoblastoma

This retrospective multicenter study examines therapy-induced orbital and ocular MRI findings in retinoblastoma patients following selective intra-arterial chemotherapy (SIAC) and quantifies the impact of SIAC on ocular and optic nerve growth. Patients were selected based on medical chart review, with inclusion criteria requiring the availability of posttreatment MR imaging encompassing T2-weighted and T1-weighted images (pre- and post-intravenous gadolinium administration). Qualitative features and quantitative measurements were independently scored by experienced radiologists, with deep learning segmentation aiding total eye volume assessment. Eyes were categorized into three groups: eyes receiving SIAC (Rb-SIAC), eyes treated with other eye-saving methods (Rb-control), and healthy eyes. The most prevalent adverse effects post-SIAC were inflammatory and vascular features, with therapy-induced contrast enhancement observed in the intraorbital optic nerve segment in 6% of patients. Quantitative analysis revealed significant growth arrest in Rb-SIAC eyes, particularly when treatment commenced ≤ 12 months of age. Optic nerve atrophy was a significant complication in Rb-SIAC eyes. In conclusion, this study highlights the vascular and inflammatory adverse effects observed post-SIAC in retinoblastoma patients and demonstrates a negative impact on eye and optic nerve growth, particularly in children treated ≤ 12 months of age, providing crucial insights for clinical management and future research

A diffusion tensor imaging study of brain white matter in children

L’imagerie en tenseur de diffusion, ou DTI, est une application de l’imagerie de diffusion qui permet de quantifier en chaque direction de l’espace la diffusion des molécules d’eau. Cette technique permet d’obtenir la direction de fibres cérébrales en chaque voxel, et de reconstruire indirectement les faisceaux de substance blanche du cerveau en 3D par tractographie. Les paramètres scalaires du tenseur, la FA ou fraction d’anisotropie, et l’ADC ou coefficient apparent de diffusion, permettent d’analyser la microstructure cérébrale de manière quantifiée. Les applications du DTI sont nombreuses, comme l’étude du développement cérébral normal et des pathologies de la substance blanche.Nous avons tout d’abord étudié le DTI chez le fœtus. Pour ce faire, une chaîne de traitement d’images DTI fœtales, compilée dans un logiciel, Baby Brain Toolkit (BTK) (https://github.com/rousseau/fbrain), a été implémentée. Ce logiciel permet notamment de corriger les artéfacts de mouvements qui dégradent la qualité du DTI fœtal. BTK a été validé sur des cas normaux, puis a été appliqué à un modèle de malformation cérébrale. Nous avons aussi étudié un cas d’infection à cytomégalovirus en DTI.Nous avons ensuite analysé l’intérêt des paramètres scalaires DTI dans l’étude d’une leucodystrophie rare, le syndrome de Cockayne. Le DTI permet de diagnostiquer le syndrome de Cockayne, de distinguer ses sous-types cliniques, et d’approcher sa physiopathologie. Nous avons ainsi montré qu’il s’agit d’une pathologie hypomyélinisante primitive, suivie d’une démyélinisation secondaire de bas grade.Diffusion tensor imaging (DTI) is a diffusion-weighted imaging application that allows water motion quantification in any direction. This technique determines brain fiber direction in each voxel, and reconstructs indirectly white matter fibers tracts in 3D with tractography. Scalar DTI parameters, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC), provide a quantitative analysis of brain microstructure. DTI applications are numerous, especially in the study of brain development and white matter pathologies.First, we studied DTI in the fetus. For this, we implemented a processing method for fetal DTI images, and compiled it in a software, Baby brain Toolkit (BTK) (https://github.com/rousseau/fbrain). BTK was validated on normal cases, and then applied to a brain malformation model. We also studied a case of cytomegalovirus infection with DTI.We then investigated the utility of scalar DTI parameters in a rare leukodystrophy, Cockayne syndrome. DTI allows to diagnose Cockayne syndrome, to distinguish between clinical subtypes, and to understand its pathophysiology. We showed that Cockayne syndrome was a primitive hypomyelinating disorder, followed by a low grade secondary demyelination

A diffusion tensor imaging study of brain white matter in children

L’imagerie en tenseur de diffusion, ou DTI, est une application de l’imagerie de diffusion qui permet de quantifier en chaque direction de l’espace la diffusion des molécules d’eau. Cette technique permet d’obtenir la direction de fibres cérébrales en chaque voxel, et de reconstruire indirectement les faisceaux de substance blanche du cerveau en 3D par tractographie. Les paramètres scalaires du tenseur, la FA ou fraction d’anisotropie, et l’ADC ou coefficient apparent de diffusion, permettent d’analyser la microstructure cérébrale de manière quantifiée. Les applications du DTI sont nombreuses, comme l’étude du développement cérébral normal et des pathologies de la substance blanche.Nous avons tout d’abord étudié le DTI chez le fœtus. Pour ce faire, une chaîne de traitement d’images DTI fœtales, compilée dans un logiciel, Baby Brain Toolkit (BTK) (https://github.com/rousseau/fbrain), a été implémentée. Ce logiciel permet notamment de corriger les artéfacts de mouvements qui dégradent la qualité du DTI fœtal. BTK a été validé sur des cas normaux, puis a été appliqué à un modèle de malformation cérébrale. Nous avons aussi étudié un cas d’infection à cytomégalovirus en DTI.Nous avons ensuite analysé l’intérêt des paramètres scalaires DTI dans l’étude d’une leucodystrophie rare, le syndrome de Cockayne. Le DTI permet de diagnostiquer le syndrome de Cockayne, de distinguer ses sous-types cliniques, et d’approcher sa physiopathologie. Nous avons ainsi montré qu’il s’agit d’une pathologie hypomyélinisante primitive, suivie d’une démyélinisation secondaire de bas grade.Diffusion tensor imaging (DTI) is a diffusion-weighted imaging application that allows water motion quantification in any direction. This technique determines brain fiber direction in each voxel, and reconstructs indirectly white matter fibers tracts in 3D with tractography. Scalar DTI parameters, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC), provide a quantitative analysis of brain microstructure. DTI applications are numerous, especially in the study of brain development and white matter pathologies.First, we studied DTI in the fetus. For this, we implemented a processing method for fetal DTI images, and compiled it in a software, Baby brain Toolkit (BTK) (https://github.com/rousseau/fbrain). BTK was validated on normal cases, and then applied to a brain malformation model. We also studied a case of cytomegalovirus infection with DTI.We then investigated the utility of scalar DTI parameters in a rare leukodystrophy, Cockayne syndrome. DTI allows to diagnose Cockayne syndrome, to distinguish between clinical subtypes, and to understand its pathophysiology. We showed that Cockayne syndrome was a primitive hypomyelinating disorder, followed by a low grade secondary demyelination

Produits de contraste en IRM et leur interêt dans la pathologie secondaire du foie, notamment d'origine colorectale

STRASBOURG-Medecine (674822101) / SudocSTRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Etude de la substance blanche cérébrale de l'enfant par imagerie en tenseur de diffusion

L imagerie en tenseur de diffusion, ou DTI, est une application de l imagerie de diffusion qui permet de quantifier en chaque direction de l espace la diffusion des molécules d eau. Cette technique permet d obtenir la direction de fibres cérébrales en chaque voxel, et de reconstruire indirectement les faisceaux de substance blanche du cerveau en 3D par tractographie. Les paramètres scalaires du tenseur, la FA ou fraction d anisotropie, et l ADC ou coefficient apparent de diffusion, permettent d analyser la microstructure cérébrale de manière quantifiée. Les applications du DTI sont nombreuses, comme l étude du développement cérébral normal et des pathologies de la substance blanche.Nous avons tout d abord étudié le DTI chez le fœtus. Pour ce faire, une chaîne de traitement d images DTI fœtales, compilée dans un logiciel, Baby Brain Toolkit (BTK) (https://github.com/rousseau/fbrain), a été implémentée. Ce logiciel permet notamment de corriger les artéfacts de mouvements qui dégradent la qualité du DTI fœtal. BTK a été validé sur des cas normaux, puis a été appliqué à un modèle de malformation cérébrale. Nous avons aussi étudié un cas d infection à cytomégalovirus en DTI.Nous avons ensuite analysé l intérêt des paramètres scalaires DTI dans l étude d une leucodystrophie rare, le syndrome de Cockayne. Le DTI permet de diagnostiquer le syndrome de Cockayne, de distinguer ses sous-types cliniques, et d approcher sa physiopathologie. Nous avons ainsi montré qu il s agit d une pathologie hypomyélinisante primitive, suivie d une démyélinisation secondaire de bas grade.Diffusion tensor imaging (DTI) is a diffusion-weighted imaging application that allows water motion quantification in any direction. This technique determines brain fiber direction in each voxel, and reconstructs indirectly white matter fibers tracts in 3D with tractography. Scalar DTI parameters, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC), provide a quantitative analysis of brain microstructure. DTI applications are numerous, especially in the study of brain development and white matter pathologies.First, we studied DTI in the fetus. For this, we implemented a processing method for fetal DTI images, and compiled it in a software, Baby brain Toolkit (BTK) (https://github.com/rousseau/fbrain). BTK was validated on normal cases, and then applied to a brain malformation model. We also studied a case of cytomegalovirus infection with DTI.We then investigated the utility of scalar DTI parameters in a rare leukodystrophy, Cockayne syndrome. DTI allows to diagnose Cockayne syndrome, to distinguish between clinical subtypes, and to understand its pathophysiology. We showed that Cockayne syndrome was a primitive hypomyelinating disorder, followed by a low grade secondary demyelination.STRASBOURG-Bib.electronique 063 (674829902) / SudocSudocFranceF

MSI detection and its pitfalls in CMMRD syndrome in a family with a bi-allelic MLH1 mutation

International audienc