Enhancing Medical Imaging Semantic Segmentation Using the Digital Annealer

Deep convolutional neural networks (DCNNs) are demonstrating their strong capability in solving computer vision problems. As for medical imaging semantic segmentation, DCNN models have become one of the most fundamental constituent. However, high-quality semantic segmentation requires pixel-wise prediction with high precision, which usually cannot be achieved only by DCNNs due to their lack of representation of pixel interactions. In this work, we propose a novel method based on conditional random fields (CRFs) and Ising model. It applies the Digital Annealer (DA) as a complement to traditional methods that only employ DCNNs. Our experiment results manifest that the use of DA can enhance the segmentation accuracy on BRATS2012 data set by over 8%. Our work potentially builds a new pathway in this realm.