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Background: A crucial initial processing step for quantitative PET/CTanalysis is the segmentation of tumor lesions enabling accurate featureex-traction, tumor characterization, oncologic staging, and image-based therapyresponse assessment. Manual lesion segmentation is however associated withenormous effort and cost and is thus infeasible in clinical routine. Goal: Thegoal of this study was to report the performance of a deep neural networkdesigned to automatically segment regions suspected of cancer in whole-body18F-FDG PET/CT images in the context of the AutoPET challenge. Method: Acascaded approach was developed where a stacked ensemble of 3D UNET CNNprocessed the PET/CT images at a fixed 6mm resolution. A refiner networkcomposed of residual layers enhanced the 6mm segmentation mask to the originalresolution. Results: 930 cases were used to train the model. 50% werehistologically proven cancer patients and 50% were healthy controls. Weobtained a dice=0.68 on 84 stratified test cases. Manual and automaticMetabolic Tumor Volume (MTV) were highly correlated (R2 = 0.969,Slope = 0.947).Inference time was 89.7 seconds on average. Conclusion: The proposed algorithmaccurately segmented regions suspicious for cancer in whole-body 18F -FDGPET/CT images.

Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks