Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
Deep learning for segmentation in radiation therapy planning: a review
Segmentation of organs and structures, as either targets or organs‐at‐risk, has a significant
influence on the success of radiation therapy. Manual segmentation is a tedious and time …
influence on the success of radiation therapy. Manual segmentation is a tedious and time …
Artificial intelligence: resha** the practice of radiological sciences in the 21st century
Advances in computing hardware and software platforms have led to the recent resurgence
in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for …
in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for …
DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy
Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …
Automatic detection of contouring errors using convolutional neural networks
Purpose To develop a head and neck normal structures autocontouring tool that could be
used to automatically detect the errors in autocontours from a clinically validated …
used to automatically detect the errors in autocontours from a clinically validated …
Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search
OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where
inconsistencies across radiation oncologists and prohibitive labor costs motivate automated …
inconsistencies across radiation oncologists and prohibitive labor costs motivate automated …
[HTML][HTML] Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy
Z Liu, X Liu, H Guan, H Zhen, Y Sun, Q Chen… - Radiotherapy and …, 2020 - Elsevier
Purpose The delineation of the clinical target volume (CTV) is a crucial, laborious and
subjective step in cervical cancer radiotherapy. The aim of this study was to propose and …
subjective step in cervical cancer radiotherapy. The aim of this study was to propose and …
[HTML][HTML] Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based …
Purpose To develop a deep learning model that generates consistent, high-quality lymph
node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an …
node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an …
The emergence of artificial intelligence within radiation oncology treatment planning
Background: The future of artificial intelligence (AI) heralds unprecedented change for the
field of radiation oncology. Commercial vendors and academic institutions have created AI …
field of radiation oncology. Commercial vendors and academic institutions have created AI …
[HTML][HTML] Deep learning for automatic target volume segmentation in radiation therapy: a review
Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing
trend in medical imaging and become the state-of-the-art method in various clinical …
trend in medical imaging and become the state-of-the-art method in various clinical …