Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network. Issue 1 (13th October 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Medical physics. Volume 50:Issue 1(2023)
• Page Start: 274
• Page End: 283
• Publication Date: 2022-10-13
• Subjects: deep learning -- lung tumor -- PET/CT -- radiotherapy -- segmentation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16001
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: 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, whereas 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, that is, 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 inputs, 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 fivefold cross‐validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center‐of‐mass distance; (2) Bland–Altman analysis and volumetric Pearson correlation analysis. Results: In fivefold 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 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.