mlsanityData to 7 Oct 2026

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

arXiv 2102.10446 ↗First posted 20 Feb 2021eess.IV cs.CVcited by 20

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Abstract (arXiv)

Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on different centers, as well as on the test set (DSC 0.759, precision 0.833, recall 0.740) that allowed us to win first prize in the HECKTOR challenge among 21 participating teams. The full implementation based on PyTorch and the trained models are available at https://github.com/iantsen/hecktor

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 20 Feb 2021.

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5 of 11 references.

References · 5

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187

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Citing papers per year: 2021: 3; 2022: 6; 2023: 6; 2024: 3; 2025: 2.

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  1. 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 29
  2. 2021Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challengecited by 7
  3. 2021Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attentioncited by 6
  4. 2022Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?cited by 6
  5. 2023H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentationcited by 6
  6. 2023SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Imagescited by 5
  7. 2024Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuningcited by 5
  8. 2022ISA-Net: Improved spatial attention network for PET-CT tumor segmentationcited by 3
  9. 20213-D PET Image Generation with tumour masks using TGANcited by 2
  10. 2022Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networkscited by 2
  11. 2024Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Modelcited by 2
  12. 2022Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GANcited by 1
8 more
  1. 2023Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentationcited by 1
  2. 2024Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancercited by 1
  3. 2025A Comprehensive Study on Medical Image Segmentation using Deep Neural Networkscited by 1
  4. 2022Super Images -- A New 2D Perspective on 3D Medical Imaging Analysiscited by 0
  5. 2023Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patientscited by 0
  6. 2023Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligencecited by 0
  7. 2023Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentationcited by 0
  8. 2025Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applicationscited by 0