Head and Neck Tumor Segmentation First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took p...
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| Other Authors: | , , |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
12603 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-030-67194-5 |
Table of Contents:
- Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging
- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks
- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network
- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images
- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images
- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge
- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions
- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images
- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.