Automatic brain tumor segmentation by subject specific modification of atlas priors1

Abstract

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

Unifying the analyses of anatomical and diffusion tensor images using volume‐preserved warping

“…Automated segmentation has been an elusive goal in the processing of medical images, leaving expert anatomical knowledge as the sole basis for the manual segmentation or definition of brain ROIs. The manual definition of ROIs, however, is problematic because different experts usually produce different segmentations of the same image (5), and even the same expert will produce differing segmentations when segmenting the same image twice (6). The reliability and validity of segmentation can suffer even when subdividing highly discrete anatomical structures, such as the CC.…”

“…These methods, however, have not been integrated with methods for the automated delineation of ROIs as a basis for fiber tracking. Other groups, in contrast, have attempted to reduce inaccuracies in the delineation of ROIs by generating an atlas of the brain through the averaging of manual segmentations from a group of experts (6), and then using that atlas as a template for automated ROI delineation in additional subjects. These methods for reducing inaccuracies in ROI delineation, however, do not address the problem of coregistering images across anatomical and DTI datasets.…”

Monitoring slowly evolving tumors

“…The time lapse between scan two and three was very small so that we would expect small differences between the measurements. The volume changes estimated by the radiologist, 71.7mm 3 and −25.3mm 3 , differ, while the automatic measurements, especially of INTENSITY, only slightly change.…”

“…One can compute volume change by separately segmenting each scan in the time series via automatic tumor segmenters, such as [3,4]. This type of analysis, however, is very sensitive to intra-rater variability.…”

“…Unlike [1], our metric returns a quantitative analysis of the differences in volume (mm 3 ). Our approach also differs significantly from works on segmentation, such as [3,4], as we estimate volume change by simultaneously analyzing the sequence of scans.…”