CGBO-Net: Cruciform structure guided and boundary-optimized lymphoma segmentation network.

Lymphoma segmentation plays an important role in the diagnosis and treatment of lymphocytic tumor. Most current existing automatic segmentation methods are difficult to give precise tumor boundary and location. Semi-automatic methods are usually combined with manually added features such as bounding box or points to locate the tumor. Inspired by this, we propose a cruciform structure guided and boundary-optimized lymphoma segmentation network(CGBS-Net). The method uses a cruciform structure extracted based on PET images as an additional input to the network, while using a boundary gradient loss function to optimize the boundary of the tumor. Our method is divided into two main stages: In the first stage, we use the proposed axial context-based cruciform structure extraction (CCE) method to extract the cruciform structures of all tumor slices. In the second stage, we use PET/CT and the corresponding cruciform structure as input in the designed network (CGBO-Net) to extract tumor structure and boundary information. The Dice, Precision, Recall, IOU and RVD are 90.7%, 89.4%, 92.5%, 83.1% and 4.5%, respectively. Validate on the lymphoma dataset and publicly available head and neck data, our proposed approach is better than the other state-of-the-art semi-segmentation methods, which produces promising segmentation results.