Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks
This addresses the need for efficient cancer diagnosis and staging in clinical settings, though it is incremental as it builds on existing deep learning methods for medical imaging.
The study tackled the problem of automating tumor segmentation in whole-body PET/CT images to reduce manual effort, achieving a dice score of 0.68 on test cases and high correlation (R2=0.969) between manual and automatic tumor volumes.
Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Goal: The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images in the context of the AutoPET challenge. Method: A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution. A refiner network composed of residual layers enhanced the 6mm segmentation mask to the original resolution. Results: 930 cases were used to train the model. 50% were histologically proven cancer patients and 50% were healthy controls. We obtained a dice=0.68 on 84 stratified test cases. Manual and automatic Metabolic Tumor Volume (MTV) were highly correlated (R2 = 0.969,Slope = 0.947). Inference time was 89.7 seconds on average. Conclusion: The proposed algorithm accurately segmented regions suspicious for cancer in whole-body 18F -FDG PET/CT images.