Jean-Louis Dillenseger

Bio: Jean-Louis Dillenseger is an academic researcher from University of Rennes. The author has contributed to research in topics: Segmentation & Image registration. The author has an hindex of 16, co-authored 121 publications receiving 1192 citations. Previous affiliations of Jean-Louis Dillenseger include Southeast University & University of Rennes 1.

Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets

Abstract: Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM’13 workshop, in conjunction with MICCAI’13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

Liver Vessels Segmentation Using a Hybrid Geometrical Moments/Graph Cuts Method

Abstract: This paper describes a fast and fully automatic method for liver vessel segmentation on computerized tomography scan preoperative images. The basis of this method is the introduction of a 3-D geometrical moment-based detector of cylindrical shapes within the minimum-cut/maximum-flow energy minimization framework. This method represents an original way to introduce a data term as a constraint into the widely used Boykov's graph cuts algorithm, and hence, to automate the segmentation. The method is evaluated and compared with others on a synthetic dataset. Finally, the relevancy of our method regarding the planning of a necessarily accurate percutaneous high-intensity focused ultrasound surgical operation is demonstrated with some examples.

Wavelet Analysis of EEG for Three-Dimensional Mapping of Epileptic Events

Abstract: This paper is aimed at understanding epileptic patient disorders through the analysis of surface electroencephalograms (EEG). It deals with the detection of spikes or spike-waves based on a nonorthogonal wavelet transform. A multilevel structure is described that locates the temporal segments where abnormal events occur. These events are then visually interpreted by means of a 3D mapping technique. This 3D display makes use of a ray tracing scheme and combines both the functional (the EEG but also its wavelet representation) and the morphological data (acquired from computed tomography [CT] or magnetic resonance imaging [MRI] devices). The results show that a significant reduction of the clinical workload is obtained while the most important episodes are better reviewed and analyzed.

A moment-based three-dimensional edge operator

Abstract: A three-dimensional edge operator for detecting anatomical structures in medical imaging is presented. It uses the spatial moments of the gray-level surface, and operates in three dimensions with any window size. It allows the location and the contrast surface, as well as the surface orientation, to be estimated. The computation of the discrete version is reported. Bias and errors due to the spatial sampling and noise are analyzed at both a theoretical and experimental level. The moment-based operator is compared with other well-known edge operators using simple shaped primitives for which the analytical solution is known. The 3-D rendering of real data is then provided by merging the operator in a ray-tracing framework. >

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

A reflectance model for computer graphics

Abstract: This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Abstract: Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.