Biomedical subjects

Vincent Agnus

Publications and source records attributed to Vincent Agnus.

4 recordsLinked to original sources

PubMed2006

A modular and evolutive component oriented software architecture for patient modeling.

This paper deals with the design aspect of a software aiming at modeling the anatomical and pathological structures of patients from medical images, for diagnosis purposes. In terms of functionalities, it allows to combine image processing algorithms, and to visualize and manipulate 3D models and images. The proposed software uses specific extensible and reusable components and a system managing their combination, thanks to a formal XML-based description of their interfaces. This architecture facilitates the dynamic integration of new functionalities, in particular in terms of image processing algorithms. We describe the structural and behavioral aspects of the proposed reusable component-based architecture. We also discuss the potential of this work for developing other softwares in the field of computer aided surgery.

Computer Simulation↗

PubMed2006

An interactive medical image segmentation system based on the optimal management of regions of interest using topological medical knowledge.

This paper presents an original interactive system for efficient medical image segmentation in computer aided diagnosis. The main originality concerns the method used to manage, according to an a priori topological-based structural model, regions of interest (ROIs) within which computations can be constrained. The goal is then to avoid the processing of irrelevant image points, therefore improving and accelerating segmentations. In the case of a hierarchical modeling procedure, our ROI management method enables, for delineating a given medical structure, to optimally determine image points of interest by taking previously segmented structures into account. We propose a mathematical formulation of the method as well as a possible implementation within an interactive system. We also detail an experience report focussing on the segmentation of several abdominal structures from a CT image. It illustrates the behavior and the potential of our method.

Abdomen↗

PubMed2005

An efficient and generic extension to ITK to process arbitrary shaped regions of interest.

The paper describes a software method to extend ITK (Insight ToolKit, supported by the National Library of Medicine), leading to ITK++. This method, which is based on the extension of the iterator design pattern, allows the processing of regions of interest with arbitrary shapes, without modifying the existing ITK code. We experimentally evaluate this work by considering the practical case of the liver vessel segmentation from CT-scan images, where it is pertinent to constrain processings to the liver area. Experimental results clearly prove the interest of this work: for instance, the anisotropic filtering of this area is performed in only 16 s with our proposed solution, while it takes 52 s using the native ITK framework. A major advantage of this method is that only add-ons are performed: this facilitates the further evaluation of ITK++ while preserving the native ITK framework.

Algorithms↗

PubMed2005

Liver registration for the follow-up of hepatic tumors.

In this paper we propose a new two step method to register the liver from two acquisitions. This registration helps experts to make an intra-patient follow-up for hepatic tumors. Firstly, an original and efficient tree matching is applied on different segmentations of the vascular system of a single patient. These vascular systems are segmented from CT-scan images acquired (every six months) during disease treatement, and then modeled as trees. Our method matches common bifurcations and vessels. Secondly, an estimation of liver deformation is computed from the results of the first step. This approach is validated on a large synthetic database containing cases with various deformation and segmentation problems. In each case, after the registration process, the liver recovery is very accurate (around 95%) and the mean localization error for 3D landmarks in liver is small (around 4 mm).

Algorithms↗