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

• URL: http://arxiv.org/abs/2210.08068v1
• Date: Fri, 14 Oct 2022 19:25:56 GMT
• Title: Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks
• Authors: Ludovic Sibille, Xinrui Zhan, and Lei Xiang
• Abstract summary: 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. A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution.
• Score: 2.735686397209314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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