Hierarchies and shape-space for PET image segmentation

International audiencePositron Emission Tomography (PET) image segmentation is essential for detecting lesions and quantifying their metabolic activity. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. In this article, we show how the hierarchical approaches proposed in mathematical morphology can efficiently handle these different strategies. Our contribution is twofold. First, we present the component-tree as a relevant data-structure for developing interactive , real-time, intensity-based segmentation of PET images. Second, we prove that thanks to the recent concept of shaping, we can efficiently involve a priori knowledge for lesion segmentation, while preserving the good properties of component-tree segmenta-tion. Preliminary experiments on synthetic and real PET images of lymphoma demonstrate the relevance of our approach

Learning optimal shape representations for multi-modal image registration

International audienc

Shape-based analysis on component-graphs for multivalued image processing

International audienceConnected operators based on hierarchical image models have been increasingly considered for the design of efficient image segmentation and filtering tools in various application fields. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to any grey-level or multi- valued images. The notion of shaping was also introduced as a way to improve the anti-extensive filtering by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extends the component-tree from a conceptual point of view) can be associated for the effective processing of multival- ued images. We provide structural and algorithmic developments. Although the contributions of this article are mainly theoretical and methodological, we finally provide an illustration example that qualitatively emphasizes the potential use and usefulness of the proposed paradigms for actual image analysis purposes

Automated 3D lymphoma lesion segmentation from PET/CT characteristics

International audiencePositron Emission Tomography (PET) using 18 F-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantifica-tion of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation remains a challenge in PET, due to the limitations the modality suffers from, despite being essential for quantifying reliable changes in tumour tissues. Due to the spatial and spectral properties of PET images , most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the seg-mentation process. However, the current routinely-used approach remains a local relative thresholding and requires important user interaction , leading to a process that is not only user-dependent but very laborious in the case of lymphomas. In this paper, we propose to rely on hierarchical image models embedding multimodality PET/CT de-scriptors for a fully automated PET lesion detection / segmenta-tion, performed via a machine learning process. More precisely, we propose to perform random forest classification within the mixed spatial-spectral space of component-trees modeling PET/CT mages. This new approach, combining the strengths of machine learning and morphological hierarchy models leads to intelligent thresholding based on high-level PET/CT knowledge. We evaluate our approach on a database of multi-centric PET/CT images of patients treated for lymphoma, delineated by an expert. Our method provides good efficiency, with the detection of 92% of all lesions, and accurate seg-mentation results with mean sensitivity and specificity of 0.73 and 0.99 respectively, without any user interaction

Segmentation 3D des lésions du lymphome à partir de descripteurs multimodaux TEP/TDM

National audienc

Shape-based analysis on component-graphs for multivalued image processing

International audienceThe extension of mathematical morphology to multivalued images is an important issue. This is particularly true in the context of connected operators based on morphological hierarchies, which aim to provide efficient image filtering and segmentation tools in various application fields, e.g. (bio)medical imaging, remote sensing, or astronomy. In this article, we propose a preliminary study that describes how two notions recently introduced for connected filtering, namely component-graphs (that extend component-trees from a spectral point of view) and shaping (that extend component-trees from a conceptual point of view) can be associated for the effective processing of multivalued images. Structural, algo-rithmic and experimental developments are proposed. This study opens the way to new paradigms for connected filtering based on hierarchies

Analysis of lymph node tumor features in PET/CT for segmentation

International audienceIn the context of breast cancer, the detection and segmentation of cancerous lymph nodes in PET/CT imaging is of crucial importance, in particular for staging issues. In order to guide such image analysis procedures, some dedicated descriptors can be considered, especially region-based features. In this article, we focus on the issue of choosing which features should be embedded for lymph node tumor segmentation from PET/CT. This study is divided into two steps. We first investigate the relevance of various features by considering a Random Forest framework. In a second time, we validate the expected relevance of the best scored features by involving them in a U-Net segmentation architecture. We handle the region-based definition of these features thanks to a hierarchical modeling of the PET images. This analysis emphasizes a set of features that can significantly improve / guide the segmentation of lymph nodes in PET/CT

Hiérarchies et analyse dans l'espace des formes pour la segmentation des images de tomographie par émission de positons

International audienc

Shape‐based analysis on component‐graphs for multivalued image processing

National audienc

Regularized Multi-Label Fast Marching and Application to Whole-Body Image Segmentation

International audienceIn this paper, we propose a computationally efficient regularization strategy for the Fast Marching (FM) segmentation of multiple organs. Segmentation is based on interactive seeds placements, where the seeds define either organs of interest or the background. Regularization efficiently compensates for the sensitivity of the FM to narrow bridges between different organs with similar intensities. It also leads to segmentations that are far less sensitive to seeds location than by using the standard FM cost. The driving motivation of our work is the quantitative analysis of Chronic Lymphocytic Leukemia (CLL), which is the most common B-cell malignancy and mostly affects elderly people. This requires the segmentation of more than ten organs in whole-body Magnetic Resonance images. The disease path and progression being highly heterogeneous with important inter-patient variability, the segmentation is highly based on clinician experience, which is difficult to automatically reproduce. In this context, the proposed segmentation algorithm compares very favourably to the tools usually available for clinicians