Spinal Neoplasm Image Inpainting with Deep Convolutional Neutral Networks Spinal tumor is an abnormal mass of tissue in or surrounding the spinal cord or spinal column that is difficult to dissection for its complicated anatomic location. At present, Total En Bloc Spondylectomy (TES) is an effective treatment which can remove tumor lesions with tumorfree surgical margin and maintain the mechanical stability of the spine. This paper aims to address the spinal Computed Tomography (CT) image inpainting to reconstruct a normal and complete vertebral body used for TES by 3D printing. Firstly, the images of spine CT are segmented by neural network to obtain binary images. Then, the generative adversarial network (GAN) is trained using binary images of normal spinal CT. Next, the region of the lesion is detected from CT images with the spinal tumor by the deep convolutional neural network. Finally, the constructed GAN model is applied for the image inpainting of the spinal tumor CTs. As a result, the restored CT images are reconstructed in three dimensions to obtain a normal spine model. The experimental results prove that the proposed method is capable of reconstructing practical vertebral bodies and evolving potential applications.

Semisupervised Attentive Mutualinfo Generative Adversarial Network for Brain Tumor Segmentation Recent years have witnessed tremendous success in medical image analysis using deep learning methods. However, most of the work are based on fullysupervised learning, which requires sizeable and reliable labeled data. This is one of the major bottlenecks in applying these methods into practice. To reduce the labeling burden, semisupervised learning that enables models to be learned from both labeled data and unlabeled data has been applied in many tasks, including medical image analysis. This work presents a semisupervised attentive mutual info generative adversarial network (SAMGAN) for brain tumor segmentation. SAMGAN is injected with comprehensive attention mechanism and a novel local mutual information maximization mechanism. The attention module contains channel attention block and spatial attention block, where the channel attention block indicates what to focus, while the spatial attention block indicates where to focus. Moreover, the mutual information maximization mechanism is used to focus the network on the local dependencies of the images, leading to more powerful representation ability of the network. Experimental results on two brain tumor segmentation tasks show that the proposed SAM GAN outperforms traditional fullysupervised methods and other semisupervised competitors, which shows the effectiveness of the proposed model.