Hugues Talbot

In Proceedings of the 5th international workshop, BrainLes 2019, held in conjunction with MICCAI 2019

Abstract

The work presented in this paper addresses the MICCAI BraTS 2019 challenge devoted to brain tumor segmentation using mag- netic resonance images. For each task of the challenge, we proposed and submitted for evaluation an original method. For the tumor segmentation task (Task 1), our convolutional neural network is based on a variant of the U-Net architecture of Ronneberger et al. with two modifications: first, we separate the four convolution parts to decorrelate the weights corresponding to each modality, and second, we provide volumes of size 240 * 240 * 3 as inputs in these convolution parts. This way, we profit of the 3D aspect of the input signal, and we do not use the same weights for separate inputs. For the overall survival task (Task 2), we compute explainable features and use a kernel PCA embedding followed by a Random Forest classifier to build a predictor with very few training samples. For the uncertainty estimation task (Task 3), we introduce and compare lightweight methods based on simple principles which can be applied to any segmentation approach. The overall performance of each of our contribution is honorable given the low computational requirements they have both for training and testing.

Spherical fluorescent particle segmentation and tracking in 3D confocal microscopy

By Élodie Puybareau, Edwin Carlinet, Alessandro Benfenati, Hugues Talbot

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract

Spherical fluorescent particle are micrometer-scale spherical beads used in various areas of physics, chemistry or biology as markers associated with local physical media. They are useful for example in fluid dynamics to characterize flows, diffusion coefficients, viscosity or temperature; they are used in cells dynamics to estimate mechanical strain and stress at the micrometer scale. In order to estimate these physical measurements, tracking these particles is necessary. Numerous approaches and existing packages, both open-source and proprietary are available to achieve tracking with a high degree of precision in 2D. However, little such software is available to achieve tracking in 3D. One major difficulty is that 3D confocal microscopy acquisition is not typically fast enough to assume that the beads are stationary during the whole 3D scan. As a result, beads may move between planar scans. Classical approaches to 3D segmentation may yield objects are not spherical. In this article, we propose a 3D bead segmentation that deals with this situation.

High throughput automated detection of axial malformations in Medaka embryo

By Diane Genest, Élodie Puybareau, Marc Léonard, Jean Cousty, Noémie De Crozé, Hugues Talbot

2019-01-22

In Computers in Biology and Medicine

Abstract

Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction based on mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to detect the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the presence of axial malformations. We built and validated our learning model on 1459 images with a 10-fold cross-validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis..

High throughput automated detection of axial malformations in fish embryo

By Diane Genest, Élodie Puybareau, Jean Cousty, Marc Leonard, Hugues Talbot, Noemie De Croze

2018-11-29

In

Abstract

Fish embryo models are widely used as screening tools to assess the efficacy and /or toxicity of chemicals. This assessment involves analysing embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction due to mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to focus on the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the detection of axial malformations. We built and validated our learning model on 1,459 images with a 10-fold cross- validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. Indeed, most of the errors are due to the inherent limitations of 2D images compared to 3D observations. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.

Caractérisation des zones de mouvement périodiques pour applications bio-médicales

By Élodie Puybareau, Hugues Talbot, Laurent Najman

2017-06-28

In Actes du 26e colloque GRETSI

Abstract

De nombreuses applications biomedicales impliquent l’analyse de séquences pour la caractérisation du mouvement. Dans cet article, nous considerons des séquences 2D+t où un mouvement particulier (par exemple un flux sanguin) est associé à une zone spécifique de l’image 2D (par exemple une artère). Mais de nombreux mouvements peuvent co-exister dans les séquences (par exemple, il peut y avoir plusieurs vaisseaux sanguins presents, chacun avec leur flux spécifique). La caractérisation de ce type de mouvement implique d’abord de trouver les zones où le mouvement est présent, puis d’analyser ces mouvements : vitesse, régularité, fréquence, etc. Dans cet article, nous proposons une méthode appropriée pour détecter et caractériser simultanément les zones où le mouvement est présent dans une séquence. Nous pouvons ensuite classer ce mouvement en zones cohérentes en utilisant un apprentissage non supervisé et produire des métriques directement utilisables pour diverses applications. Nous illustrons et validons cette même méthode sur l’analyse du flux sanguin chez l’embryon de poisson.

Morphological analysis of brownian motion for physical measurements

By Élodie Puybareau, Hugues Talbot, Noha Gaber, Tarik Bourouina

2017-02-23

In Mathematical morphology and its application to signal and image processing – proceedings of the 13th international symposium on mathematical morphology (ISMM)

Abstract

Brownian motion is a well-known, apparently chaotic mo- tion affecting microscopic objects in fluid media. The mathematical and physical basis of Brownian motion have been well studied but not often exploited. In this article we propose a particle tracking methodology based on mathematical morphology, suitable for Brownian motion analysis, which can provide difficult physical measurements such as the local temperature and viscosity. We illustrate our methodology on simulation and real data, showing that interesting phenomena and good precision can be achieved.

Periodic area-of-motion characterization for bio-medical applications

By Élodie Puybareau, Hugues Talbot, Laurent Najman

2017-02-20

In Proceedings of the IEEE international symposium on bio-medical imaging (ISBI)

Abstract

Many bio-medical applications involve the analysis of sequences for motion characterization. In this article, we consider 2D+t sequences where a particular motion (e.g. a blood flow) is associated with a specific area of the 2D image (e.g. an artery) but multiple motions may exist simultaneously in the same sequences (e.g. there may be several blood vessels present, each with their specific flow). The characterization of this type of motion typically involves first finding the areas where motion is present, followed by an analysis of these motions: speed, regularity, frequency, etc. In this article, we propose a methodology called “area-of-motion characterization” suitable for simultaneously detecting and characterizing areas where motion is present in a sequence. We can then classify this motion into consistent areas using unsupervised learning and produce directly usable metrics for various ap- plications. We illustrate this methodology for the analysis of cilia motion on ex-vivo human samples, and we apply and validate the same methodology for blood flow analysis in fish embryo.