Brain MR imaging segmentation using convolutional auto encoder network for PET attenuation correction

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-55190-2_32© Springer Nature Switzerland AG 2021. Magnetic resonance (MR) image segmentation is one of the most robust MR based attenuation correction methods which have been adopted in clinical routine for positron emission tomography (PET) quantification. However, the segmentation of the brain into different tissue classes is a challenging process due to the similarity between bone and air signal intensity values. The aim of this work is to study the feasibility of deep learning to improve the brain segmentation with the application of data augmentation. A deep convolutional auto encoder network is applied to segment the brain into three tissue classes: air, soft tissue, and bone. The dice similarity coefficients of air, soft tissue, and bone tissues are 0.96 ± 0.01, 0.86 ± 0.02, and 0.63 ± 0.06 respectively. Despite the small datasets used in this work, the results are promising and show the feasibility of deep learning with data augmentation to perform accurate segmentation

Medical Image Synthesis via Deep Learning

Medical images have been widely used in clinics, providing visual representations of under-skin tissues in human body. By applying different imaging protocols, diverse modalities of medical images with unique characteristics of visualization can be produced. Considering the cost of scanning high-quality single modality images or homogeneous multiple modalities of images, medical image synthesis methods have been extensively explored for clinical applications. Among them, deep learning approaches, especially convolutional neural networks (CNNs) and generative adversarial networks (GANs), have rapidly become dominating for medical image synthesis in recent years. In this chapter, based on a general review of the medical image synthesis methods, we will focus on introducing typical CNNs and GANs models for medical image synthesis. Especially, we will elaborate our recent work about low-dose to high-dose PET image synthesis, and cross-modality MR image synthesis, using these models