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In Medical physics ; h5-index 59.0

BACKGROUND : Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning.

PURPOSE : In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images.

METHODS : The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities while the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, i.e., a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net and a score head. Given a PET/CT image as input, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A five-fold cross-validation study was performed. The segmentation was evaluated with two indicators, 1) multiple metrics including the Dice similarity coefficient (DSC), Jacard, 95th percentile Hausdorff distance (HD95 ), mean surface distance (MSD), residual mean square distance (RMSD), and center-of-mass distance (COMD); 2) Bland-Altman analysis and volumetric Pearson correlation analysis.

RESULTS : In five-fold cross-validation, this method achieved Dice and MSD of 0.84±0.15 and 1.38±2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive (TCIA) dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods.

CONCLUSION : The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort. This article is protected by copyright. All rights reserved.

Lei Yang, Wang Tonghe, Jeong Jiwoong J, Janopaul-Naylor James, Kesarwala Aparna H, Roper Justin, Tian Sibo, Bradley Jeffrey D, Liu Tian, Higgins Kristin, Yang Xiaofeng


PET/CT, deep learning, lung tumor, radiotherapy, segmentation