C2BA-UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation

Tumor segmentation is a necessary step in clinical processing that can help doctors diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations and appearances vary substantially across individuals, and the contrast between tumors and adjacent normal tissues is low, tumor segmentation is still a challenging task. Although convolutional neural networks (CNNs) have achieved good results in tumor segmentation, the information about tumor boundaries has been rarely explored. To solve the problem, this paper proposes a new method for automatic tumor segmentation in PET/CT images based on context-coordination and boundary-aware, termed as C2BA-UNet. We employ a UNet-like backbone network and replace the encoder with EfficientNet-B0 for efficiency. To acquire potential tumor boundaries, we propose a new multi-atlas boundary-aware (MABA) module based on gradient atlas, uncertainty atlas, and level set atlas, that focuses on uncertain regions between tumors and adjacent tissues. Furthermore, we propose a new context coordination module (CCM) to combine multi-scale context information with attention mechanism to optimize skip connection in high-level layers. To validate the superiority of our method, we conduct experiments on a publicly available soft tissue sarcoma (STS) dataset and a lymphoma dataset, and the results show our method is competitive with other comparison methods.