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Segmentation of lymphoma lesions is challenging due to their varied sizes andlocations in whole-body PET scans. This work presents a fully-automatedsegmentation technique using a multi-center dataset of diffuse large B-celllymphoma (DLBCL) with heterogeneous characteristics. We utilized a dataset of[18F]FDG-PET scans (n=194) from two different imaging centers, including caseswith primary mediastinal large B-cell lymphoma (PMBCL) (n=104). Automated brainand bladder removal approaches were utilized as preprocessing steps to tacklefalse positives caused by normal hypermetabolic uptake in these organs. Oursegmentation model is a convolutional neural network (CNN) based on a 3D U-Netarchitecture that includes squeeze and excitation (SE) modules. Hybriddistribution, region, and boundary-based losses (Unified Focal and Mumford-Shah(MS)) were utilized that showed the best performance compared to othercombinations (p<0.05). Cross-validation between different centers, DLBCL andPMBCL cases, and three random splits were applied on train/validation data. Theensemble of these six models achieved a Dice similarity coefficient (DSC) of0.77 +- 0.08 and Hausdorff distance (HD) of 16.5 +-12.5. Our 3D U-net modelwith SE modules for segmentation with hybrid loss performed significantlybetter (p<0.05) as compared to the 3D U-Net (without SE modules) using the sameloss function (Unified Focal and MS loss) (DSC= 0.64 +-0.21 and HD= 26.3 +-18.7). Our model can facilitate a fully automated quantification pipeline in amulti-center context that opens the possibility for routine reporting of totalmetabolic tumor volume (TMTV) and other metrics shown useful for the managementof lymphoma.

Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images