Abstract
.Purpose: We introduce and evaluate deep learning methods for weakly supervised segmentation of tumor lesions in whole-body fluorodeoxyglucose-positron emission tomography (FDG-PET) based solely on binary global labels (“tumor” versus “no tumor”).Approach: We propose a three-step approach based on (i) a deep learning framework for image classification, (ii) subsequent generation of class activation maps (CAMs) using different CAM methods (CAM, GradCAM, GradCAM++, ScoreCAM), and (iii) final tumor segmentation based on the aforementioned CAMs. A VGG-based classification neural network was trained to distinguish between PET image slices with and without FDG-avid tumor lesions. Subsequently, the CAMs of this network were used to identify the tumor regions within images. This proposed framework was applied to FDG-PET/CT data of 453 oncological patients with available manually generated ground-truth segmentations. Quantitative segmentation performance was assessed for the different CAM approaches and compared with the manual ground truth segmentation and with supervised segmentation methods. In addition, further biomarkers (MTV and TLG) were extracted from the segmentation masks.Results: A weakly supervised segmentation of tumor lesions was feasible with satisfactory performance [best median Dice score 0.47, interquartile range (IQR) 0.35] compared with a fully supervised U-Net model (median Dice score 0.72, IQR 0.36) and a simple threshold based segmentation (Dice score 0.29, IQR 0.28). CAM, GradCAM++, and ScoreCAM yielded similar results. However, GradCAM led to inferior results (median Dice score: 0.12, IQR 0.21) and was likely to ignore multiple instances within a given slice. CAM, GradCAM++, and ScoreCAM yielded accurate estimates of metabolic tumor volume (MTV) and tumor lesion glycolysis. Again, worse results were observed for GradCAM.Conclusions: This work demonstrated the feasibility of weakly supervised segmentation of tumor lesions and accurate estimation of derived metrics such as MTV and tumor lesion glycolysis.
Highlights
Contrast-enhanced computed tomography (CT) remains the backbone for oncological staging, whereas 18-fluordesoxyglucose ([18F]-FDG) positron emission tomography (PET)/CT hybrid imaging plays a central role in the detection of distant metastatic disease.[1]
We propose a framework for weakly supervised segmentation of tumor lesions in fullbody PET/CT images of patients with cancer
GradCAM++ performed slightly worse with a median Dice score of 0.42 (IQR 0.30)
Summary
Contrast-enhanced computed tomography (CT) remains the backbone for oncological staging, whereas 18-fluordesoxyglucose ([18F]-FDG) positron emission tomography (PET)/CT hybrid imaging plays a central role in the detection of distant metastatic disease.[1]. Significant progress has been made in the automatic analysis of medical images, mainly due to the emergence of deep learning methods.[4,5] Deep learning models have already been successfully applied for the detection and segmentation of tumor lesions.[6] Established approaches are mostly based on supervised learning schemes[7] that use a large amount of manually voxel-wise annotated ground-truth data. We evaluate our proposed approach for different CAM methods and compare its performance in predicting TLG and MTV with supervised segmentation approaches for PET/CT images of oncological patients with lung cancer, lymphoma, and malignant melanoma
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