Automated multimodal volume registration based on supervised 3D anatomical landmark detection

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

Collaborative analysis of multi-gigapixel imaging data using Cytomine

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

Machine Learning for Landmark Detection in Biomedical Applications

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

Enregistrement automatisé du volume multimodal basé sur la détection anatomique de repère 3D

peer reviewedWe 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

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows

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