Author
Johan Montagnat
Other affiliations: University of Lyon, French Institute of Health and Medical Research, University of Nice Sophia Antipolis ...read more
Bio: Johan Montagnat is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Workflow & Grid. The author has an hindex of 36, co-authored 176 publications receiving 4250 citations. Previous affiliations of Johan Montagnat include University of Lyon & French Institute of Health and Medical Research.
Papers published on a yearly basis
Papers
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TL;DR: A survey on deformable surfaces identifies the main representations proposed in the literature and studies the influence of the representation on the model evolution behavior, revealing some similarities between different approaches.
319 citations
TL;DR: A fully automatic anatomical, pathological, and functional segmentation of the liver derived from a spiral CT scan is developed to improve the planning of hepatic surgery.
Abstract: Objective: To improve the planning of hepatic surgery, we have developed a fully automatic anatomical, pathological, and functional segmentation of the liver derived from a spiral CT scan.Materials and Methods: From a 2 mm-thick enhanced spiral CT scan, the first stage automatically delineates skin, bones, lungs, kidneys, and spleen by combining the use of thresholding, mathematical morphology, and distance maps. Next, a reference 3D model is immersed in the image and automatically deformed to the liver contours. Then an automatic Gaussian fitting on the imaging histogram estimates the intensities of parenchyma, vessels, and lesions. This first result is next improved through an original topological and geometrical analysis, providing an automatic delineation of lesions and veins. Finally, a topological and geometrical analysis based on medical knowledge provides hepatic functional information that is invisible in medical imaging: portal vein labeling and hepatic anatomical segmentation according to the C...
300 citations
01 Aug 2008
TL;DR: This paper describes the design and implementation of MOTEUR, a workflow engine that fulfills the need for well-defined data composition strategies on the one hand and for a fully parallel execution on the other.
Abstract: Workflows offer a powerful way to describe and deploy applications on grid infrastructures. Many workflow management systems have been proposed but there is still a lack of a system that would allow both a simple description of the dataflow of the application and an efficient execution on a grid platform. In this paper, we study the requirements of such a system, underlining the need for well-defined data composition strategies on the one hand and for a fully parallel execution on the other. As combining those features is not straightforward, we then propose algorithms to do so and we describe the design and implementation of MOTEUR, a workflow engine that fulfills those requirements. Performance results and overhead quantification are shown to evaluate MOTEUR with respect to existing comparable workflow systems on a production grid.
208 citations
TL;DR: In this paper, deformable surfaces are represented as simplex meshes owing to their generality and their ability to compute mean curvature at each vertex in order to extend the deformable surface framework by introducing time-dependent constraints.
204 citations
TL;DR: Three algorithms related to the control of the contour topology, geometry, and deformation are introduced and demonstrated on several images including medical images and a comparison with the level-sets method is provided.
133 citations
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9,362 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
TL;DR: The rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking is reviewed.
2,222 citations
TL;DR: A fully-automated segmentation method that uses manually labelled image data to provide anatomical training information and is assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively,Using an independent clinical dataset involving Alzheimer's disease.
2,047 citations
TL;DR: This work proposes a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D Dense UNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation.
Abstract: Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
1,561 citations