ArticleThe British journal of radiology2021
Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- TCIA Radiology Image Processing for AI and Radiomics.medRxiv : the preprint server for health sciences · 2026Article
- Physician-guided deep learning model for assessing thymic epithelial tumor volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Anatomy guided modality fusion for cancer segmentation in PET CT volumes and images.Scientific reports · 2025Article
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.Medical physics · 2025Article
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- Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images.Physics and imaging in radiation oncology · 2024Article
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- An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images.PeerJ. Computer science · 2024Article
- Review
- SIFT-GVF-based lung edge correction method for correcting the lung region in CT images.PloS one · 2023Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Article
- Review
- A Review of Artificial Intelligence Application for Radiotherapy.Dose-response : a publication of International Hormesis SocietyReview
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12 authors.
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Abstract
objectiveA stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning.
methodsIn this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder-decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored.
resultsThe newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics (
conclusionsThe implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. ADVANCES IN KNOWLEDGE: The hybrid CNN has valuable prospect with the ability to segment lung tumor.
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