Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

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 using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.

Intervention of carbonate components in petrogenesis of the pyroxene nephelinites from the Jbel Saghro (Anti-Atlas, Morocco)

The Jbel Saghro alkaline complex was emplaced close to the eastern edge of the Moroccan Anti-Atlas. Within the northern part, two types of nephelinite were recognized [Ibhi and Nachit, 1999 and lbhi, 2000]. The first type (olivine-rich nephelinite) constitutes the main volcanic mass south of the Bou Gafer granit (fig. 1), where the volcanism had been active at least during 2 Ma, between 9.6 and 7.5 +/- 0.1 Ma [Berrahma et al., 1993]. The second group outcrops in the north (Foum El Kouss). It consists of pyroxene nephelinites which are younger (2.9 +/- 0.1 Ma) [Berrahma et al., 1993], and bears carbonatitic xenoliths, melteigitic pyroxenites and metasomatised peridotite xenoliths. Geochemically, the pyroxene nephelinite is highly enriched in LILE compared with the first one. The mineralogical and geochemical characteristics may be explained by the incorporation of carbonatitic and melteigitic pyroxenite segregates of carbonatitic affinity.

An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

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 visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Long-term outcomes of 118 patients with eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome) enrolled in two prospective trials.

The purpose of this study was to assess the outcomes of 118 patients with eosinophilic granulomatosis with polyangiitis (EGPA) enrolled in 2 prospective, randomized, open-label clinical trials (1994-2005), with or without Five-Factor Score (FFS)-defined poor-prognosis factors, focusing on survival, disease-free survival, relapses, clinical and laboratory findings, therapeutic responses, and factors predictive of relapse. Forty-four patients with FFS ≥ 1 were assigned to receive 6 or 12 cyclophosphamide pulses plus corticosteroids and the seventy-four with FFS = 0 received corticosteroids alone, with immunosuppressant adjunction when corticosteroids failed. Patients were followed (2005-2011) under routine clinical care in an extended study and data were recorded prospectively. Mean ± SD follow-up was 81.3 ± 39.6 months. Among the 118 patients studied, 29% achieved long-term remission and 10% died. Among the 115 patients achieving a first remission, 41% experienced ≥1 relapses, 26.1 ± 26.8 months after treatment onset, with 57% of relapses occurring when corticosteroid-tapering reached <10 mg/day. Treatment achieved new remissions in >90%, but relapses recurred in 38%. Overall survival was good, reaching 90% at 7 years, regardless of baseline severity. Age ≥65 years was the only factor associated with a higher risk of death during follow-up. The risk of relapse was higher for patients with anti-myeloperoxidase antibodies and lower for those with >3000 eosinophils/mm(3). Sequelae remained frequent, usually chronic asthma and peripheral neuropathy. In conclusion, EGPA patients' survival rate is very good when treatment is stratified according to the baseline FFS. Relapses are frequent, especially in patients with anti-myeloperoxidase antibodies and baseline eosinophilia <3000/mm(3).

A review of atlas-based segmentation for magnetic resonance brain images.

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.

Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework.

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 perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.