ArticleJournal of digital imaging2019
Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- A Meta-Analysis of Deep-Learning-Based Fusion of Positron Emission Tomography and Computed Tomography in Oncologic Imaging.Journal of imaging informatics in medicine · 2026Review
- Auto-segmentation of the clinical target volume using a domain-adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy.Precision radiation oncology · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.Frontiers in oncology · 2023Article
- Multi-scale brain tumor segmentation combined with deep supervision.International journal of computer assisted radiology and surgery · 2022Review
- Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.Frontiers in oncology · 2022Article
- BSMM-Net: Multi-modal neural network based on bilateral symmetry for nasopharyngeal carcinoma segmentation.Frontiers in human neuroscience · 2022Article
- DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.European journal of nuclear medicine and molecular imaging · 2021Article
- Changes of [BMC medical imaging · 2021Article
- Artificial intelligence in molecular imaging.Annals of translational medicine · 2021Review
- Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Application and Construction of Deep Learning Networks in Medical Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.Molecular imaging and biology · 2020Article
- Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.European journal of hybrid imaging · 2020Review
- Comparison of rigid and deformable image registration for nasopharyngeal carcinoma radiotherapy planning with diagnostic position PET/CT.Japanese journal of radiology · 2020Article
- A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences.Computational and mathematical methods in medicine · 2020Article
- A Review of Artificial Intelligence Application for Radiotherapy.Dose-response : a publication of International Hormesis SocietyReview
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.
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