Skip to main content
SpringerLink
Log in

Head and Neck Tumor Segmentation

First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Vincent Andrearczyk

    1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Valentin Oreiller

    1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Adrien Depeursinge

    1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12603)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: HECKTOR 2020.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (11 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

    • Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Martin Vallières, Joel Castelli, Hesham Elhalawani et al.
    Pages 1-21

  3. Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET Imaging

    • Simeng Zhu, Zhenzhen Dai, Ning Wen
    Pages 22-27

  4. The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel ‘Squeeze & Excitation’ Blocks

    • Juanying Xie, Ying Peng
    Pages 28-36

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

    • Andrei Iantsen, Dimitris Visvikis, Mathieu Hatt
    Pages 37-43

  6. Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network

    • Yading Yuan
    Pages 44-52

  7. Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT Images

    • Huai Chen, Haibin Chen, Lisheng Wang
    Pages 53-58

  8. Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET Images

    • Jun Ma, Xiaoping Yang
    Pages 59-64

  9. Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge

    • Chinmay Rao, Suraj Pai, Ibrahim Hadzic, Ivan Zhovannik, Dennis Bontempi, Andre Dekker et al.
    Pages 65-77

  10. Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions

    • Kanchan Ghimire, Quan Chen, Xue Feng
    Pages 78-84

  11. Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images

    • Mohamed A. Naser, Lisanne V. van Dijk, Renjie He, Kareem A. Wahid, Clifton D. Fuller
    Pages 85-98

  12. GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images

    • Fereshteh Yousefirizi, Arman Rahmim
    Pages 99-108

  13. Back Matter

    Pages 109-109
    Download chapter PDF
Back to top

Other volumes

  1. Head and Neck Tumor Segmentation

Keywords

About this book

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 place virtually due to the COVID-19 pandemic.

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.

Editors and Affiliations

  • HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation