Cascaded deep learning‐based auto‐segmentation for head and neck cancer patients: organs at risk on T2‐weighted magnetic resonance imaging
Purpose To investigate multiple deep learning methods for automated segmentation (auto‐
segmentation) of the parotid glands, submandibular glands, and level II and level III lymph …
segmentation) of the parotid glands, submandibular glands, and level II and level III lymph …
[HTML][HTML] Automatic tumor segmentation with a convolutional neural network in multiparametric mri: Influence of distortion correction
L Bielak, N Wiedenmann, NH Nicolay, T Lottner… - Tomography, 2019 - ncbi.nlm.nih.gov
Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional
neural networks (CNNs) are among the highest scoring automatic approaches for tumor …
neural networks (CNNs) are among the highest scoring automatic approaches for tumor …
Feasibility of Margin Reduction for Level II and III Planning Target Volume in Head-and-Neck Image-Guided Radiotherapy–Dosimetric Assessment via A Deformable …
X Sharon Qi, J Neylon, S Can, R Staton… - Current Cancer …, 2014 - ingentaconnect.com
Purposes: To improve normal tissue sparing for head-and-neck (H&N) image-guided
radiotherapy (IGRT) by employing treatment plans with tighter margins for CTV 2 and 3, and …
radiotherapy (IGRT) by employing treatment plans with tighter margins for CTV 2 and 3, and …
[HTML][HTML] Intermodality Variability in Gross Tumor Volume Delineation for Radiation Therapy Planning in Oropharyngeal Squamous Cell Carcinoma
TS Fathima, P Sethi, G Ramkumar, D Halanaik… - Advances in Radiation …, 2024 - Elsevier
Purpose Multimodality imaging can enhance the precision of tumor delineation for intensity
modulated radiation therapy planning. This study aimed to analyze intermodality variation …
modulated radiation therapy planning. This study aimed to analyze intermodality variation …
[HTML][HTML] Characterizing geometrical accuracy in clinically optimised 7T and 3T magnetic resonance images for high-precision radiation treatment of brain tumours
J Peerlings, I Compter, F Janssen, CJ Wiggins… - Physics and Imaging in …, 2019 - Elsevier
Background and purpose In neuro-oncology, high spatial accuracy is needed for clinically
acceptable high-precision radiation treatment planning (RTP). In this study, the clinical …
acceptable high-precision radiation treatment planning (RTP). In this study, the clinical …
SCARF: Auto-Segmentation Clinical Acceptability & Reproducibility Framework for Benchmarking Essential Radiation Therapy Targets in Head and Neck Cancer
J Marsilla, J Won Kim, D Tkachuck, S Kim, J Siraj… - medRxiv, 2022 - medrxiv.org
Abstract Background and Purpose Auto-segmentation of organs at risk (OAR) in cancer
patients is essential for enhancing radiotherapy planning efficacy and reducing inter …
patients is essential for enhancing radiotherapy planning efficacy and reducing inter …
MR‐linac daily semi‐automated end‐to‐end quality control verification
Purpose Adaptive radiation therapy (ART) on the integrated Elekta Unity magnetic
resonance (MR)‐linac requires routine quality assurance to verify delivery accuracy and …
resonance (MR)‐linac requires routine quality assurance to verify delivery accuracy and …
[引用][C] Exploiting Deep Learning to Enhance Tumour-conformed Delineation and Reduced Isotropic Margin in Radiotherapy: Updated ESTRO-EANO Guidelines
MH Hannisdal, D Goplen, A Lundervold… - Clinical …, 2023 - clinicaloncologyonline.net
Sir d We recently published a study [1] investigating the feasibility of the HD-GLIO
autosegmentation tool [2] for radiotherapy target delineation of grade 4 glioma, using routine …
autosegmentation tool [2] for radiotherapy target delineation of grade 4 glioma, using routine …
Accuracy of automatic structure propagation for daily magnetic resonance image-guided head and neck radiotherapy
Background and purpose Deformable image registration (DIR) and contour propagation are
used in daily online adaptation for hybrid MRI linac (MRL) treatments. The accuracy of the …
used in daily online adaptation for hybrid MRI linac (MRL) treatments. The accuracy of the …
Mitigating misalignment in MRI-to-CT synthesis for improved synthetic CT generation: an iterative refinement and knowledge distillation approach
L Zhou, X Ni, Y Kong, H Zeng, M Xu… - Physics in Medicine …, 2023 - iopscience.iop.org
Objective. Deep learning has shown promise in generating synthetic CT (sCT) from
magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has …
magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has …