Automatic brain MRI segmentations methods are useful but computationally intensive tools in medical image computing. Deploying them on grid infrastructures can provide an efficient resource for data handling and computing power. In this study, an efficient implementation of a brain MRI segmentation method through a grid-interfaced workflow enactor is proposed. The deployment of the workflow enables simultaneous processing and validation. The importance of parallelism is shown with concurrent analysis of several MRI subjects. The results obtained from the grid have been compared to the results computed locally on only one computer. Thanks to the power of the grid, method's parameter influence on the resulting segmentations has also been assessed given the best compromise between algorithm speed and results accuracy. This deployment highlights also the grid issue of a bottleneck effect.