Prevalence, Clinical Management, and Natural Course of Incidental Findings on Brain MR Images: The Population-based Rotterdam Scan Study.

Purpose To present an updated prevalence estimate for incidental findings on brain magnetic resonance (MR) images and provide information on clinical relevance, including natural course, over a period of up to 9 years. Materials and Methods This study was approved by the institutional review board and all participants gave informed consent. In a prospective population-based setting, structural brain MR imaging was performed in 5800 participants (mean age, 64.9 years; 3194 women [55.1%]). Trained reviewers recorded abnormalities, which were subsequently evaluated by neuroradiologists. The prevalence with 95% confidence interval (CI) of incidental findings was determined, and clinical management of findings that required the attention of a medical specialist was followed. Follow-up imaging in the study context provided information on the natural course of findings that were not referred. Results In 549 of 5800 participants (9.5% [95% CI: 8.7%, 10.3%]), incidental findings were found, of which meningiomas (143 of 5800; 2.5% [95% CI: 2.1%, 2.9%]) and cerebral aneurysms (134 of 5800; 2.3% [95% CI: 2.0%, 2.7%]) were most common. A total of 188 participants were referred to medical specialists for incidental findings (3.2% [95% CI: 2.8%, 3.7%]). Of these, 144 (76.6% [95% CI: 70.1%, 82.1%]) either underwent a wait-and-see policy or were discharged after the initial clinical visit. The majority of meningiomas and virtually all aneurysms not referred or referred but untreated remained stable in size during follow-up. Conclusion Incidental findings at brain MR imaging that necessitate further diagnostic evaluation occur in over 3% of the general middle-aged and elderly population, but are mostly without direct clinical consequences. © RSNA, 2016.

Automated brain structure segmentation based on atlas registration and appearance models.

Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure's location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structure's appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.