Identification of boundaries in MRI medical images using artificial neural networks

In the area of medical imaging, fully-automatic and robust segmentation techniques would have an enormous beneficial impact on clinical practice and research, by decreasing dramatically the manual effort which must otherwise be devoted to this task. Deployment of conventional image processing techniques has not so far led to a fully-automatic solution, although semi-automatic systems do exist. Since no known, robust segmentation algorithm exists, the ability of neural networks to discover regularities and features in complex data is appealing. Indeed, many preliminary attempts at neural segmentation have been described, although none yet achieves the necessary level of performance for routine application. Southampton General Hospital have a requirement to obtain lung-boundary data within an asthma research project. In connection with this requirement, we have previously reported on work in which multilayer perceptrons (MLPs) are trained using backpropagation to segment the region of the lungs in magnetic resonance images of the thorax. This is achieved by training the network to classify voxels as either boundary (voxels on the boundary between lung interior and surrounding tissue) or non-boundary. In this paper, we present the latest results using this technique. We also show how the generalisation performance of the MLP can be improved using a variety of techniques, including weight pruning algorithms