Valérie Duay

• PhD
• Professor (Associate) at HES-SO University of Applied Sciences and Arts Western Switzerland

Image segmentation is a main task in many medical applications such as surgical or radiation therapy planning, automatic labelling of anatomical structures or morphological and morphometrical studies. Segmentation in medical imaging is however challenging because of problems linked to low contrast images, fuzzy object-contours, similar intensities...

CONGRES SUISSE DE RADIOLOGIE 12 -14 juin 2014 CONFERENCE « ATELIER » – MENTION « ESPACE FORMATION » Nouvelles dimensions dans l’apprentissage interactif de l’anatomie radiologique. E. Fleury1, E. Ambrosetti1, J. Marquis1, J. Schmid1, C. Chênes1, V. Duay1, A. Naïmi1, S. Varone1, O. Ratib2, J. Fasel3. HES-SO – Genève (1) Hôpitaux Universitaires de...

Titre : Nouvelles dimensions dans l'apprentissage interactif de l'anatomie radiologique Introduction : En général les médecins praticiens et les professionnels de la santé ont peu accès à l’anatomie dans leur pratique clinique alors que ces notions d’anatomie deviennent de plus en plus importantes aujourd’hui étant donné la place prépondérante pri...

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to p...

This paper proposes to apply the non parametric atlas registration framework we have recently developed in [6]. This technique derived from the optical flow model and the active contour framework allows to base the registration of an anatomical atlas on selected structures. A well-suited application of our model is the non rigid registration of med...

This paper presents automated segmentation of structures in the Head and Neck (H\&N) region, using an active contour-based joint registration and segmentation model. A new atlas selection strategy is also used. Segmentation is performed based on the dense deformation field computed from the registration of selected structures in the atlas image tha...

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; instead, they are segmented using the dense deformation field computed from the registration of...

This paper presents automated segmentation of structures in the Head and Neck (H\&N) region, using an active contour-based joint registration and segmentation model. A new atlas selection strategy is also used. Segmentation is performed based on the dense deformation field computed from the registration of selected structures in the atlas image tha...

We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the probability for each point to belong to the object. This statistical map is treated as a prior dist...

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding vi...

A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance application generally requires to extract moving objects (vehicles, persons or animals) from a seq...

Medical imaging poses the great challenge of having compression algorithms that are lossless for diagnostic and legal reasons and yet provide high compression rates for reduced storage and transmission time. The images usually consist of a region of interest representing the part of the body under investigation surrounded by a "background", which i...

In this paper, we present two models for supervised scalar image segmentation based on the active contours and information theory. First we propose to carry out a region competition by optimizing an energy designed to be minimal when the entropy of the inside and outside regions of the evolving active contour are close to those of a reference image...

In this paper, we present a new paradigm to carry out the nonrigid registration of multiple regions with a dense deformation field derived from the optical flow model and the active contour framework. The method can merge different tasks such as registration, segmentation and incorporation of prior knowledge into a single framework. The technique i...

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform...

In this paper, we propose a new paradigm to carry out the registration task with a dense deformation field derived from the optical flow model and the active contour method. The proposed framework merges different tasks such as segmentation, regularization, incorporation of prior knowledge and registration into a single framework. The active contou...

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the pa...

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surf...

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the pa...

Atlas-based medical image segmentation is a well known method for including prior knowledge in medical image analysis. It requires as basic component the registration of an atlas with the image. In this paper, we introduce the concept of hierarchical atlas and show how to efficiently include it in a state-of-the-art non-rigid registration algorithm...

Non-rigid registration algorithms have been proposed over the years to register medical images to each other. One class of applications for these algorithms is the automatic segmentation of structures and substructures using a predefined atlas. But these algorithms have been limited to image volumes without gross abnormalities or pathologies and ha...

This paper presents a fully automated brain segmentation method that has been applied to a group of patients with infratentorial ependymoma. The purpose of the study was to test the hypothesis that fully-automated atlas-based segmentation methods provide useful normal tissue dosimetry from which dose-volume modeling may be performed in a manner equ...

Measurement of intra-operative brain motion is important to provide boundary conditions to physics-based deformation models that can be used to register pre- and intra-operative information. In this paper we present and test a technique that can be used to measure brain surface motion automatically. This method relies on a tracked laser range scann...

A novel brain shift tracking protocol is introduced in this paper which utilizes laser range scan (LRS) data and 2D deformable image registration. This protocol builds on previous efforts to incorporate intra-operative LRS data into a model-updated image guided surgery paradigm for brain shift compensation. The shift tracking method employs the use...

Conformally prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. The current clinical practice of manually segmenting brain structures can be complex and exceedingly time consuming. Automatic computeraided segmentat...

Delineation of structures to irradiate (the tumors) as well as structures to be spared (e.g., optic nerve, brainstem, or eyes) is required for advanced radiotherapy techniques. Due to a lack of time and the number of patients to be treated these cannot always be segmented accurately which may lead to suboptimal plans. A possible solution is to deve...

Measurement of intra-operative brain motion is important to provide boundary conditions to physics-based deformation models that can be used to register pre- and intra-operative information. In this paper we present and test a technique that can be used to measure brain surface motion automatically. This method relies on a tracked laser range scan-...

This paper presents a fully automated brain segmentation method that has been applied to a group of patients with infratentorial ependymoma. The purpose of the study was to test the hypothesis that fully-automated atlas-based segmentation methods provide useful normal tissue dosimetry from which dose-volume modeling may be performed in a manner equ...

We propose a statistical non-parametric classification of brain tissues from an MR image based on the voxel intensities and on the relative anatomical location of the different tissues. Classically, the overlap of the tissue probability distribution functions for voxel intensities can be reduced by using multi-component (T1w,T2w,Pd,...) MR images,...

We propose a statistical nonparametric classification of brain tissues from an MR image based on the voxel intensities and on the relative anatomical location of the different tissues. We generate an artificial image component as the distance from the edges of the segmented brain. The nonparametric k-nearest neighbors rule (k-NN) is used since it r...