Opencc – an open Benchmark data set for Corpus Callosum Segmentation and Evaluation

Neuroimaging studies have revealed that the structural changes of the corpus callosum (CC) are evident in a variety of neurological diseases, such as epilepsy and autism. Segmentation of the CC from magnetic resonance images (MRI) of the brain is a crucial step in the diagnosis of various brain disorders. However, the lack of open benchmark CC datasets has hindered development of CC segmentation techniques. In this work, we present an open benchmark dataset - OpenCC - for CC segmentation and evaluation. The dataset was built through alternative application of automatic segmentation and manual refinement. The automatic segmentation is based on recent advances in deep learning - fully convolutional networks, specifically U-Net, while the manual refinement is done by domain radiologists. The resulting dataset consists of 4643 mid-sagittal (or near mid-sagittal) slices and their corresponding CC masks. Furthermore, we provided some baseline segmentation results on the OpenCC dataset by using two latest deep learning segmentation approaches. The OpenCC dataset can be used for comparison and evaluation of newly developed CC segmentation algorithms. We endeavor that, through the publishing of the OpenCC dataset and baseline segmentation results, we could promote further development of CC segmentation techniques.