Purpose: A comparative treatment planning study has been undertaken between standard photon delivery techniques,b intensity modulated photon methods and spot scanned protons in order to investigate the merits and limitations of each of these treatment approaches.Methods: Plans for each modality were performed using CT scans and planning information for nine patients with varying indications and lesion sites and the results have been analysed using a variety of dose and volume based parameters.Results: Over all cases, it is predicted that the use of protons could lead to a reduction of the total integral dose by a factor three compared to standard photon techniques and a factor two compared to IM photon plans. In addition, in all but one Organ at Risk (OAR) for one case, protons are predicted to reduce both mean OAR dose and the irradiated volume at the 50% mean target dose level compared to both photon methods. However, when considering the volume of an OAR irradiated to 70% or more of the target dose, little difference could be shown between proton and intensity modulated photon plans. On comparing the magnitude of dose hot spots in OARs resulting from the proton and IM photon plans, more variation was observed, and the ranking of the plans was then found to be case and OAR dependent.Conclusions: The use of protons has been found to reduce the medium to low dose load (below about 70% of the target dose) to OARs and all non-target tissues compared to both standard and inversely planned photons, but that the use of intensity modulated photons can result in similar levels of high dose conformation to that afforded by protons. However, the introduction of inverse planning methods for protons is necessary before general conclusions on the relative ef®cacy of photons and protons can be drawn. q

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Abstract-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 Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.Index Terms-Atlas-based segmentation, label fusion, medical imaging, MRF, SBA.

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