Pierre-Alain Tercier

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In recent years, multi-atlas fusion methods have gained significant attention in medical image segmentation. In this paper, we propose a general Markov Random Field (MRF) based framework that can perform edge-preserving smoothing of the labels at the time of fusing the labels itself. More specifically, we formulate the label fusion problem with MRF-based(More)
This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour-based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of " single-most-similar " atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases(More)
In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: “Global Weighted Shape-Based Averaging” (GWSBA) and “Local Weighted Shape-Based Averaging” (LWSBA). These methods extend the well known(More)
Accurate segmentation of lymph nodes in head and neck (H&N) CT images is essential for the radiotherapy planning of the H&N cancer. Atlas-based segmentation methods are widely used for the automated segmentation of such structures. Multi-atlas approaches are proven to be more accurate and robust than using a single atlas. We have recently proposed a general(More)
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 that have(More)
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