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

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

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented

Optimisation in stereotactic radiosurgery of AVMs: II. Comparison of arc and MMLC therapy.

Two stereotactic surgery methods, arc and micro-multileave collimator (MMLC) therapy, were compared in the particular case of arteriovenous malformations (AVMs) treatment. Different methods of the treatment optimisation were used. The comparison covered a group of 22 patients suffering from peripheral and central AVMs of different sizes who underwent initially arc therapy. Several parameters were evaluated to compare the two methods: 2D and 3D isodose representations, dose-volume histograms (DVHs) and probability of success. The 3D isodoses were compared for the 22 patients showing a better conformity for the MMLC (three cases are presented). The DVHs of the AVM were also in favour of MMLC. In terms of probability of success, the results showed that are therapy was superior only in the case of small spherical lesions. MMLC therapy proved to be superior to arc therapy in all cases but central spherical small volume AVMs

Evaluation of Atlas Fusion Strategies for Segmentation of Head and Neck Lymph Nodes for Radiotherapy Planning

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 Markov random field (MRF)-based framework that can perform edge-preserving smoothing of the labels at the time of fusing the labels itself. There are three main contributions of this paper: First, we reformulate the "shape based averaging" (SBA) fusion method to fit into the general MRF-based fusion framework. Second, we evaluate the following fusion algorithms for the segmentation of H&N lymph nodes: (i) STAPLE, (ii) SBA, (iii) SBA+MRF, (iv) majority voting (MV), (v) MV+MRF, (vi) global weighted voting (GWV), (vii) GWV+MRF, (viii) local weighted voting (LWV) and (ix) LWV+MRF. Finally, we also study the effect varying the number of atlases on the performance of the above algorithms