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

arXiv: https://arxiv.org/abs/2102.10446 · HTML: https://mlsanity.com/paper/2102.10446/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)

## Built on by (most cited first, 20 of 20)

- [Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images](https://mlsanity.com/paper/2201.04138/) (2022)
- [Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge](https://mlsanity.com/paper/2110.01607/) (2021)
- [Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention](https://mlsanity.com/paper/2111.03848/) (2021)
- [Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?](https://mlsanity.com/paper/2201.06251/) (2022)
- [H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation](https://mlsanity.com/paper/2307.01486/) (2023)
- [SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images](https://mlsanity.com/paper/2302.03861/) (2023)
- [Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning](https://mlsanity.com/paper/2409.13641/) (2024)
- [ISA-Net: Improved spatial attention network for PET-CT tumor segmentation](https://mlsanity.com/paper/2211.02256/) (2022)
- [3-D PET Image Generation with tumour masks using TGAN](https://mlsanity.com/paper/2111.01866/) (2021)
- [Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks](https://mlsanity.com/paper/2202.07823/) (2022)
- [Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model](https://mlsanity.com/paper/2401.17593/) (2024)
- [Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN](https://mlsanity.com/paper/2206.06448/) (2022)
- [Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation](https://mlsanity.com/paper/2303.07093/) (2023)
- [Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer](https://mlsanity.com/paper/2409.06605/) (2024)
- [A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks](https://mlsanity.com/paper/2506.04121/) (2025)
- [Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis](https://mlsanity.com/paper/2205.02847/) (2022)
- [Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients](https://mlsanity.com/paper/2304.08106/) (2023)
- [Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence](https://mlsanity.com/paper/2306.00034/) (2023)
- [Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation](https://mlsanity.com/paper/2308.08396/) (2023)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)

## 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