Rationale and Objectives-Manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. The authors have developed an automated system for brain tumor segmentation that provides objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention.Material and Methods-The method performs the segmentation of a registered set of MR images using an Expectation-Maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject specific brain tumor prior that is computed based on contrast enhancement. Conclusion-The automated method can be applied to different types of tumors. Although the semi-automated method generates results that have higher level of agreement with the manual raters, the automatic method has the advantage of requiring no user supervision. Results-Five

Abstract. Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functions and definition of spatial constraints. A strong spatial prior, however, prevents segmentation of structures that are not part of the model. In practical applications, we encounter either the presentation of new objects that cannot be modeled with a spatial prior or regional intensity changes of existing structures not explained by the model. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension to an existing expectation maximization (EM) segmentation algorithm that modifies a probabilistic brain atlas with an individual subject's information about tumor location obtained from subtraction of post-and pre-contrast MRI. The new method handles various types of pathology, space-occupying mass tumors and infiltrating changes like edema. Preliminary results on five cases presenting tumor types with very different characteristics demonstrate the potential of the new technique for clinical routine use for planning and monitoring in neurosurgery, radiation oncology, and radiology.

Combining image segmenrarion based on statistical classificarinn wirh a geometric prior has been shown rn signifrcanrly increase robustness and reproducibiliry. Using a probabilistic geometric model and image registration serves borh inirialization fj'probability densityfuncrions and de$nirion of sparial consrrainrs. A strong spatialprioz however: prevenrs segnientarion of srriicfares rhat ure not part of the model. Oirr driving application is the sgmenrarion of brain rissue and tumors from three-dimensional magneric resonance imaging (MRI). Our goal is a high-quality segmenration of borh healrhy tissire arid rumor: We presenr an exfension ro m exisrins expecrarion rnaxitnizarion ( E M ) sepnetzration algorirhnr rhar modifres U probabilistic brain atlas wirh an individual srrbject's information about tumor locarion obrained from subrrucrion of post-and pre-conrrasr MRI. The new' merhod handles various types of pathology, space-occrrpying mass rumors and infiltrating changes like edema. Preliminan results on five cases presenring rumor "pes wirh v e q different characrerisrics demonstrare the potential of rhe nebti technique for cliriical murine use forplanning and monitoring in neiirosnrgen, radiation oncology, and radiology

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