References of "Vandaele, Rémy"
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See detailMachine Learning for Landmark Detection in Biomedical Applications
Vandaele, Rémy ULiege

Doctoral thesis (2018)

Machine Learning aims at developing models able to accurately predict an output variable given the value of some input variables by using a dataset of observed (input, output) pairs. In the recent years ... [more ▼]

Machine Learning aims at developing models able to accurately predict an output variable given the value of some input variables by using a dataset of observed (input, output) pairs. In the recent years, the development of new Machine Learning algorithms as well as the increase of computing capabilities have made these methods very popular and successful to address various image processing related tasks. One of these tasks is landmark detection, which consists in finding the coordinates of one or several interest points in images. Landmark detection finds many applications in computer vision. In this thesis, we focus on two of them, both related to bioimaging. The first is morphometrics, where landmark coordinates are used to measure the size and the shape of body parts. The second is image registration, where the coordinates of the landmarks are used to compute the deformation between two images. During this thesis, we have developed an automated landmark detection algorithm combining tree-based machine learning models with multi-resolution pixel descriptors. Starting from an algorithm used for cephalometric landmark detection, we have progressively extended it in order to fit the needs of morphometric analyzes, where a wide variety of image datasets and body types are observed. We carefully analyzed the behavior of our algorithm in order to provide detailed insights about its performance on new image datasets. We then extended our landmark detection algorithm to 3D images and used it to perform CT-CBCT rigid registration. Finally, we studied the relevance of using post-processing steps based on the landmark shape structure given the specificities of biomedical applications. Throughout this work, we evaluated our method on four different datasets: three datasets concerning 2D morphometrics, and one concerning 3D image registration. On these datasets, we showed that our algorithm could reach state of the art performance while providing additional genericity regarding its application on datasets containing different types of images. [less ▲]

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See detailLab-Meeting: the ADRIC project
Vandaele, Rémy ULiege

Conference (2018, May 15)

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See detailLandmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.
Vandaele, Rémy ULiege; Aceto, Jessica; Muller, Marc ULiege et al

in Scientific Reports (2018), 8(1), 538

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based ... [more ▼]

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research. [less ▲]

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See detailAutomated multimodal volume registration based on supervised 3D anatomical landmark detection
Vandaele, Rémy ULiege; LALLEMAND, François ULiege; MARTINIVE, Philippe ULiege et al

in SCITEPRESS Digital Library (2017)

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely ... [more ▼]

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely Randomized Trees and multi-resolution voxel windows. A least-squares fitting algorithm is then used for rigid registration based on the landmark positions as predicted by these detectors in the two imaging modalities. Experiments are carried out with this method on a dataset of pelvis CT and CBCT scans related to 45 patients. On this dataset, our fully automatic approach yields results very competitive with respect to a manually assisted state-of-the-art rigid registration algorithm. [less ▲]

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See detailDétection automatique de points d'intérêt sur des images 2D et 3D
Vandaele, Rémy ULiege

Scientific conference (2016, April 14)

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See detailCollaborative analysis of multi-gigapixel imaging data using Cytomine
Marée, Raphaël ULiege; Rollus, Loïc; Stévens, Benjamin et al

in Bioinformatics (2016)

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of ... [more ▼]

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share, and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. Availability: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/. A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. [less ▲]

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See detailEvaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge
Ching Wei, Wang; Cheng-Ta, Huang; Meng-Che, Hsieh et al

in IEEE Transactions on Medical Imaging (2015)

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See detailGIGA-Cancer: Image Registration for Radiotherapy
Vandaele, Rémy ULiege

Conference (2014, October 17)

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