A Patch-Based Segmentation Approach with High Level Representation of the Data for Cortical Sulci Recognition

Because of the strong variability of the cortical sulci, their automatic recognition is still a challenging problem. The last algorithm developed in our laboratory for 125 sulci reaches an average recognition rate around 86%. It has been applied to thousands of brains for morphometric studies (www.​brainvisa.​info). A weak point of this approach is the modeling of the training dataset as a single template of sulcus-wise probability maps, losing information about the alternative patterns of each sulcus. To overcome this limit, we propose a different strategy inspired by Multi-Atlas Segmentation (MAS) and more particularly the patch-based approaches. As the standard way of extracting patches does not seem capable of exploiting the sulci geometry and the relations between them, which we believe to be the discriminative features for recognition, we propose a new patch generation strategy based on a high level representation of the sulci. We show that our new approach is slightly, but significantly, better than the reference one, while we still have an avenue of potential refinements that were beyond reach for a single template strategy.