Improving Whole-Heart CT Image Segmentation by Attention Mechanism

Decent whole-heart segmentation from computed tomography (CT) can greatly contribute to the diagnosis and treatment of cardiovascular diseases. However, due to the difficulties such as blurred boundaries between neighbouring tissues and a large number of background voxels in medical images, automated whole-heart segmentation is still a challenging task. In this paper, we proposed three modified attention models, including simple negative example mining (SNEM), attention gate (AG) and U-CliqueNet (UCNet), to lead the deep learning network to focus on more salient information. These three attention modules were further implemented into a deeply-supervised 3D UNET separately and jointly, showing different degrees of improvement on the whole-heart segmentation task. Our experiments advised that SNEM was the most simple and effective attention mechanism for medical image processing among the three and the UCNet could reach the best performance. The combination of the attention mechanisms cannot always synergistically increase the accuracy, but joint models would have a positive influence in most cases. Finally, our network achieved a Dice score of 0.9112, which was a substantially higher performance than most of the state-of-the-art methods.