TransRUnet: 2D Detection and Segmentation of Lymphoma Lesions in Full-Body PET-CT Images

Sammanfattning: Identification and localization of FDG-avid lymphoma lesions in PET-CT image volumes is of high importance for the diagnosis and monitoring of treatment progress in lymphoma patients. This process is tedious, time-consuming, and error-prone, due to large image volumes and the heterogeneity of lesions. Thus, a fully automatic method for lymphoma detection is desirable. The AutoPET challenge dataset contains 145 full-body FDG-PET-CT images of lymphoma patients with pixel-level segmentation of lesions. The Retina U-Net utilizes semantic segmentation maps for object detection through simultaneous segmentation and detection. More recently, transformer-based methods became increasingly popular due to their good performance. Here, TransRUnet is proposed, a 2D deep neural network capable of segmentation and object detection, combining the Retina U-Net with a Feature Pyramid Transformer. Firstly, a Retina U-Net was trained as a Baseline on 2D axial slices of 116 patient volumes from the AutoPET dataset, achieving an mAP of 0.377 and a DSC of 0.737 on the 29 test patients. Secondly, the TransRUnet was trained on the same patients, achieving an mAP and DSC of 0.285 and 0.732, respectively. Performance comparison based on mAP and DSC did not show significant differences (p = 0.596 and p = 0.940, for mAP and DSC, respectively) between the Retina U-Net and the TransRUnet. Furthermore, a substantial difference in FROC between the two models could not be observed. The ground truth data should be preprocessed to reduce noise in the training data or a 3D generalization of the TransRUnet should be used to improve the detection performance.