Head and Neck Tumor Segmentation
Springer International Publishing (Verlag)
978-3-030-67193-8 (ISBN)
The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.
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.
| Erscheinungsdatum | 15.01.2021 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | X, 109 p. 32 illus., 29 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 197 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • Artificial Intelligence • automatic segmentations • Bioinformatics • computerized tomography • Computer Science • computer vision • conference proceedings • CT image • Image Analysis • Image Processing • Image Segmentation • Informatics • machine learning • Medical Images • Neural networks • Object recognition • object segmentation • pattern recognition • Research • segmentation methods • Software Design • Software engineering |
| ISBN-10 | 3-030-67193-3 / 3030671933 |
| ISBN-13 | 978-3-030-67193-8 / 9783030671938 |
| Zustand | Neuware |
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