Machine learning for auto-segmentation in radiotherapy planning

K Harrison, H Pullen, C Welsh, O Oktay, J Alvarez-Valle… - Clinical Oncology, 2022 - Elsevier
Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: from atlas‐based to deep learning methods

T Vrtovec, D Močnik, P Strojan, F Pernuš… - Medical …, 2020 - Wiley Online Library
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck
(H&N), which requires a precise spatial description of the target volumes and organs at risk …

[HTML][HTML] Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study

S Nikolov, S Blackwell, A Zverovitch, R Mendes… - Journal of medical …, 2021 - jmir.org
Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

AnatomyNet: deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

W Zhu, Y Huang, L Zeng, X Chen, Y Liu, Z Qian… - Medical …, 2019 - Wiley Online Library
Purpose Radiation therapy (RT) is a common treatment option for head and neck (HaN)
cancer. An important step involved in RT planning is the delineation of organs‐at‐risks …

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

S Nikolov, S Blackwell, A Zverovitch, R Mendes… - arXiv preprint arXiv …, 2018 - arxiv.org
Over half a million individuals are diagnosed with head and neck cancer each year
worldwide. Radiotherapy is an important curative treatment for this disease, but it requires …

Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks

B Ibragimov, L Xing - Medical physics, 2017 - Wiley Online Library
Purpose Accurate segmentation of organs‐at‐risks (OAR s) is the key step for efficient
planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we …

[HTML][HTML] Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

T Lustberg, J van Soest, M Gooding… - Radiotherapy and …, 2018 - Elsevier
Background and purpose Contouring of organs at risk (OARs) is an important but time
consuming part of radiotherapy treatment planning. The aim of this study was to investigate …

Clinically applicable deep learning framework for organs at risk delineation in CT images

H Tang, X Chen, Y Liu, Z Lu, J You, M Yang… - Nature Machine …, 2019 - nature.com
Radiation therapy is one of the most widely used therapies for cancer treatment. A critical
step in radiation therapy planning is to accurately delineate all organs at risk (OARs) to …

Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer

M Kosmin, J Ledsam, B Romera-Paredes… - Radiotherapy and …, 2019 - Elsevier
Advances in technical radiotherapy have resulted in significant sparing of organs at risk
(OARs), reducing radiation-related toxicities for patients with cancer of the head and neck …

[HTML][HTML] Automated contouring and planning in radiation therapy: what is 'clinically acceptable'?

H Baroudi, KK Brock, W Cao, X Chen, C Chung… - Diagnostics, 2023 - mdpi.com
Developers and users of artificial-intelligence-based tools for automatic contouring and
treatment planning in radiotherapy are expected to assess clinical acceptability of these …