The need for accurate and consistent ground truth hinders advances in supervised learning approaches for tumor segmentation especially in PET images. In this study, we revisited the effect of supervision level on two semi-supervised approaches based on Robust FCM (RFCM) and Mumford-Shah (MS) losses for unsupervised learning combined with labeled FCM (LFCM) and Dice loss respectively as the supervised loss terms ((RFCM + αLFCM) and (MS+ αDice)). We used a multi-center (BC and SM) dataset of lymphoma patients with heterogeneous characteristics. Our results revealed that when the test data are from a center with low contribution in training data, increasing the level of supervision results in lower segmentation performance. The performance drop of MS based semi-supervised approach was higher compared to FCM based that means the training of MS based approach is more dependent on supervised learning.
Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. In this work, we present a fully-automated segmentation technique using a multi-center dataset of diffuse large B-cell lymphoma (DLBCL) with heterogeneous characteristics. We utilized a dataset of [18F]FDG-PET scans (n=194) from two different imaging centers including cases with primary mediastinal large B-cell lymphoma (PMBCL) (n=104). Automated brain and bladder removal approaches were utilized as preprocessing steps, to tackle false positives caused by normal hypermetabolic uptake in these organs. Our segmentation model is a convolutional neural network (CNN), based on a 3D U-Net architecture that includes squeeze and excitation (SE) modules. Hybrid distribution, region, and boundary-based losses (Unified Focal and Mumford-Shah (MS)) were utilized that showed the best performance compared to other combinations (p<0.05). Cross-validation between different centers, DLBCL and PMBCL cases, and three random splits were applied on train/validation data. The ensemble of these six models achieved a Dice similarity coefficient (DSC) of 0.77 ± 0.08 and Hausdorff distance (HD) of 16.5 ±12.5. Our 3D U-net model with SE modules for segmentation with hybrid loss performed significantly better (p<0.05) as compared to the 3D U-Net (without SE modules) using the same loss 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 a multi-center context that opens the possibility for routine reporting of total metabolic tumor volume (TMTV) and other metrics shown useful for the management of lymphoma.
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