EFL1 mutations impair eIF6 release to cause Shwachman-Diamond syndrome.

Shwachman-Diamond syndrome (SDS) is a recessive disorder typified by bone marrow failure and predisposition to hematological malignancies. SDS is predominantly caused by deficiency of the allosteric regulator Shwachman-Bodian-Diamond syndrome that cooperates with elongation factor-like GTPase 1 (EFL1) to catalyze release of the ribosome antiassociation factor eIF6 and activate translation. Here, we report biallelic mutations in EFL1 in 3 unrelated individuals with clinical features of SDS. Cellular defects in these individuals include impaired ribosomal subunit joining and attenuated global protein translation as a consequence of defective eIF6 eviction. In mice, Efl1 deficiency recapitulates key aspects of the SDS phenotype. By identifying biallelic EFL1 mutations in SDS, we define this leukemia predisposition disorder as a ribosomopathy that is caused by corruption of a fundamental, conserved mechanism, which licenses entry of the large ribosomal subunit into translation.Medical Research Council, Bloodwise, Wellcome Trust, Ted’s Gang, The Connor Wright Shwachman Diamond Projec

Automatic multiple sclerosis lesion segmentation with clinical constraints

L’accessibilité d’un outil performant de segmentation des lésions de sclérose en plaques permet de fournir aux radiologues des métriques fiables et reproductibles vers une meilleure prise en charge des malades atteints.Pour rendre plus accessible ce genre d’outil en clinique, nous avons proposé des architectures de réseaux de neurones convolutifs légères et performantes capables d’apprendre sur des stations de travail abordables avec un nombre réduit d’exemples d’apprentissage, en un temps réduit, tout en limitant le risque de surapprentissage.Nous avons mis en place des techniques dans le but de réduire au minimum l’apport en données d’entraînement avec l’autoapprentissage et l’apprentissage semi-supervisé, tout en tenant compte de la qualité des données pour nous apercevoir qu’il suffisait, finalement, de très peu d’examens annotés.Nous présentons aussi, une méthode pour augmenter le nombre de petites lésions détectées qui sont plus difficiles à segmenter ainsi que plus susceptibles d’être omises par le radiologue.Le travail de thèse s’inscrit dans une démarche de recherche pour tirer au mieux parti de la segmentation automatique des lésions pour le radiologue vers une meilleure adoption de tels outils en routine clinique.The availability of a powerful tool for the segmentation of multiple sclerosis lesions makes it possible to provide radiologists with reliable and reproducible metrics for better care of affected patients.To make this kind of tool more accessible in the clinic, we proposed lightweight and powerful convolutional neural network architectures capable of learning on affordable workstations with a reduced number of training examples, in a reduced time, while limiting the risk of overfitting.We have implemented techniques to minimize the training data with self-supervision and semi-supervised learning, while taking into account the quality of the data source, to realize that very few annotated exams are needed.We also propose, in this manuscript, a method to increase the number of small lesions detected which are more difficult to segment and more likely to be missed by the radiologist.The thesis work is part of a research approach to get the best out of automatic lesion segmentation for the radiologist towards a better adoption of such tools in clinical routine

Segmentation automatique des lésions de sclérose en plaques en adéquation avec les contraintes cliniques

The availability of a powerful tool for the segmentation of multiple sclerosis lesions makes it possible to provide radiologists with reliable and reproducible metrics for better care of affected patients.To make this kind of tool more accessible in the clinic, we proposed lightweight and powerful convolutional neural network architectures capable of learning on affordable workstations with a reduced number of training examples, in a reduced time, while limiting the risk of overfitting.We have implemented techniques to minimize the training data with self-supervision and semi-supervised learning, while taking into account the quality of the data source, to realize that very few annotated exams are needed.We also propose, in this manuscript, a method to increase the number of small lesions detected which are more difficult to segment and more likely to be missed by the radiologist.The thesis work is part of a research approach to get the best out of automatic lesion segmentation for the radiologist towards a better adoption of such tools in clinical routine.L’accessibilité d’un outil performant de segmentation des lésions de sclérose en plaques permet de fournir aux radiologues des métriques fiables et reproductibles vers une meilleure prise en charge des malades atteints.Pour rendre plus accessible ce genre d’outil en clinique, nous avons proposé des architectures de réseaux de neurones convolutifs légères et performantes capables d’apprendre sur des stations de travail abordables avec un nombre réduit d’exemples d’apprentissage, en un temps réduit, tout en limitant le risque de surapprentissage.Nous avons mis en place des techniques dans le but de réduire au minimum l’apport en données d’entraînement avec l’autoapprentissage et l’apprentissage semi-supervisé, tout en tenant compte de la qualité des données pour nous apercevoir qu’il suffisait, finalement, de très peu d’examens annotés.Nous présentons aussi, une méthode pour augmenter le nombre de petites lésions détectées qui sont plus difficiles à segmenter ainsi que plus susceptibles d’être omises par le radiologue.Le travail de thèse s’inscrit dans une démarche de recherche pour tirer au mieux parti de la segmentation automatique des lésions pour le radiologue vers une meilleure adoption de tels outils en routine clinique

Investigating efficient CNN architecture for multiple sclerosis lesion segmentation

International audiencePurpose: The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists’ efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks with their own different architecture. Architectural choices are rarely explained. We aimed at presenting the relevance of a U-net-like architecture for our specific task and at building an efficient and simple model.Approach: An experimental study was performed by observing the impact of applying different mutations and deletions to a simple U-net-like architecture.Results: The power of the U-net architecture is explained by the joint benefits of using an encoder–decoder architecture and by linking them with long skip connections. Augmenting the number of convolutional layers and decreasing the number of feature maps allowed us to build an exceptionally light and competitive architecture, the minimally parameterized U-net (MPU-net), with only ∼30,000 parameters.Conclusion: The empirical study of the U-net has led to a better understanding of its architecture. It has guided the building of the MPU-net, a model far less parameterized than others (at least by a factor of seven). This neural network achieves a human-level segmentation of multiple sclerosis lesions on fluid-attenuated inversion recovery images only. It shows that this segmentation task does not necessitate overly complicated models to be achieved. This gives the opportunity to build more explainable models that can help such methods to be adopted in a clinical environment

A size-adaptative segmentation method for better detection of multiple sclerosis lesions

The automatic segmentation of multiple sclerosis lesions in Magnetic Resonance Images is an open research area aiming to bring more reproducibility in the radiological visual assessment of the disease while reducing the burden of this time-consuming task. The development of artificial intelligence has led to significant improvements in computer aided diagnosis tools for radiology. It exists several efficient approaches for the voxel-wise segmentation of multiple sclerosis lesions using artificial neural networks and convolutional neural networks in particular. However, thesmall lesions are frequently neglected by those algorithms despite their radiological importance.We propose here an adaptable method to improve the detection of small lesions. The problem of small lesions detection mainly comes from the under-representation of those lesions at a voxel level and the segmentation loss function. The presented method consists in weighting the lesion importance during the training of a convolutional neural network depending on lesion size to correct the impact of voxel lesion imbalances. The designed weighting function is configurable and can be extended to other segmentation problems.With our method, the lesion segmentation computed with the Dice score is only slightly improved but the detection sensitivity is significantly improved at the cost of a limited augmentation of lesion false positive rate. The F1 score has been substantially improved with the correct set ofparameters. The improved prediction quality of segmentation maps has been confirmed visually with the help of a radiologist on a dataset acquired in our institution.The described method improves the lesion detection by giving more importance to small lesions during the multiple sclerosis lesions segmentation learning, bringing a more accurate help for the radiologist towards a better impact for the patient care

Siamese convolutional neural network for new multiple sclerosis lesion segmentation

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Apprentissage d’un CNN sur la segmentation des lésions de sclérose en plaques avec auto-supervision

International audienc

Learning a CNN on multiple sclerosis lesion segmentation with self-supervision

Best paper awardInternational audienceMultiple Sclerosis (MS) is a chronic, often disabling, auto-immune disease affecting the central nervous system and characterized by demyelination and neuropathic alterations. Magnetic Resonance (MR) images plays a pivotal role in the diagnosis and the screening of MS. MR images identify and localize demyelinat-ing lesions (or plaques) and possible associated atrophic lesions whose MR aspect is in relation with the evolution of the disease. We propose a novel MS lesions segmentation method for MR images, based on Convolutional Neural Networks (CNNs) and partial self-supervision and studied the pros and cons of using self-supervision for the current segmentation task. Investigating the transferability by freezing the firsts convolutional layers, we discovered that improvements are obtained when the CNN is retrained from the first layers. We believe such results suggest that MRI segmentation is a singular task needing high level analysis from the very first stages of the vision process, as opposed to vision tasks aimed at day-today life such as face recognition or traffic sign classification. The evaluation of segmentation quality has been performed on full image size binary maps assembled from predictions on image patches from an unseen database

CNN for multiple sclerosis lesion segmentation: How many patients for a fully supervised method?

International audienceIn this study we propose to improve an existing artificial neural network architecture, the MPU-net, which is designed for having very few parameters for multiple sclerosis lesion segmentation on magnetic resonance images. With this improved architecture we conducted a study to assess the influence of the number of training examples on the model performance and generalization. The question behind this study is: "With an appropriate architecture, how many patients do we need?". We evaluated 9 different adaptations of the MPU-net architecture. Then, after the selection of the best architecture we learned the model multiple times with different numbers of patients and assessed its performances. The addition of deep supervision, the reduction of number of convolutional layers and the addition of regularization layers produced a more stable and performant architecture. Learnings of selected model with only 10 exams delivered performances equivalent to learnings with 23 exams. So, in our experimental setup, it is possible to learn a performant model with only 10 fully annotated examples

EFL1 mutations impair eIF6 release to cause Shwachman-Diamond syndrome

International audienceShwachman-Diamond syndrome (SDS) is a recessive disorder typified by bone marrow failure and predisposition to hematological malignancies. SDS is predominantly caused by deficiency of the allosteric regulator Shwachman-Bodian-Diamond syndrome that cooperates with elongation factor-like GTPase 1 (EFL1) to catalyze release of the ribosome antiassociation factor eIF6 and activate translation. Here, we report biallelic mutations in EFL1 in 3 unrelated individuals with clinical features of SDS. Cellular defects in these individuals include impaired ribosomal subunit joining and attenuated global protein translation as a consequence of defective eIF6 eviction. In mice, Efl1 deficiency recapitulates key aspects of the SDS phenotype. By identifying biallelic EFL1 mutations in SDS, we define this leukemia predisposition disorder as a ribosomopathy that is caused by corruption of a fundamental, conserved mechanism, which licenses entry of the large ribosomal subunit into translation