mlsanityData to 7 Oct 2026

Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention

arXiv 2101.11254 ↗First posted 27 Jan 2021eess.IV cs.CVcited by 5

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Abstract (arXiv)

Radiotherapy is the main treatment modality for nasopharynx cancer. Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy. As manual delineation is time-consuming and laborious, automatic segmentation of GTV has a potential to improve this process. Currently, most of the deep learning-based automatic delineation methods of GTV are mainly performed on medical images like CT images. However, it is challenged by the low contrast between the pathology regions and surrounding soft tissues, small target region, and anisotropic resolution of clinical CT images. To deal with these problems, we propose a 2.5D Convolutional Neural Network (CNN) to handle the difference of inplane and through-plane resolution. Furthermore, we propose a spatial attention module to enable the network to focus on small target, and use channel attention to further improve the segmentation performance. Moreover, we use multi-scale sampling method for training so that the networks can learn features at different scales, which are combined with a multi-model ensemble method to improve the robustness of segmentation results. We also estimate the uncertainty of segmentation results based on our model ensemble, which is of great importance for indicating the reliability of automatic segmentation results for radiotherapy planning.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

26 of 43 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.

5 papers.

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  2. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 28
  3. 2022AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Imagescited by 10
  4. 2021DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generationcited by 7
  5. 2024Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CTcited by 0