Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy
Depending on the clinical situation, different combinations of lymph node (LN) levels define the elective LN target volume in head-and-neck cancer (HNC) radiotherapy. The accurate auto-contouring of individual LN levels could reduce the burden and variability of manual segmentation and be used regar...
Main Authors: | Victor I. J. Strijbis, Max Dahele, Oliver J. Gurney-Champion, Gerrit J. Blom, Marije R. Vergeer, Berend J. Slotman, Wilko F. A. R. Verbakel |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Cancers |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-6694/14/22/5501 |
Similar Items
-
Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data
by: Xiaojin Gu, et al.
Published: (2023-09-01) -
Evaluating the Effectiveness of Deep Learning Contouring across Multiple Radiotherapy Centres
by: Zoe Walker, et al.
Published: (2022-10-01) -
Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer
by: Seung Yeun Chung, et al.
Published: (2023-04-01) -
Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy
by: Sean Maroongroge, et al.
Published: (2024-01-01) -
Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow
by: Lorenzo Radici, et al.
Published: (2022-12-01)