ArticleJournal of translational medicine2022
Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
- From immune desert to hot tumor: the tripartite synergy of tumor microenvironment-exosomes-immunogenic cell death in pioneering solutions.Frontiers in immunology · 2026Review
- Multi - scale context U-net for breast cancer postoperative radiotherapy in patients with brachial plexus.BMC medical imaging · 2025Article
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.Respiratory research · 2025Article
- Article
- Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung.Frontiers in oncology · 2025Article
- An Artificial Intelligence-Based Automatic Classifier for the Presence of False Lumen Thrombosis After Frozen Elephant Trunk Operation.Diagnostics (Basel, Switzerland) · 2024Article
- A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.BMC medical imaging · 2024Article
- Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.BMC medical imaging · 2023Article
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6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis paper intends to propose a method of using TransResSEUnet2.5D network for accurate automatic segmentation of the Gross Target Volume (GTV) in Radiotherapy for lung cancer.
methodsA total of 11,370 computed tomograms (CT), deriving from 137 cases, of lung cancer patients under radiotherapy developed by radiotherapists were used as the training set; 1642 CT images in 20 cases were used as the validation set, and 1685 CT images in 20 cases were used as the test set. The proposed network was tuned and trained to obtain the best segmentation model and its performance was measured by the Dice Similarity Coefficient (DSC) and with 95% Hausdorff distance (HD95). Lastly, as to demonstrate the accuracy of the automatic segmentation of the network proposed in this study, all possible mirrors of the input images were put into Unet2D, Unet2.5D, Unet3D, ResSEUnet3D, ResSEUnet2.5D, and TransResUnet2.5D, and their respective segmentation performances were compared and assessed.
resultsThe segmentation results of the test set showed that TransResSEUnet2.5D performed the best in the DSC (84.08 ± 0.04) %, HD95 (8.11 ± 3.43) mm and time (6.50 ± 1.31) s metrics compared to the other three networks.
conclusionsThe TransResSEUnet 2.5D proposed in this study can automatically segment the GTV of radiotherapy for lung cancer patients with more accuracy.
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