[HTML][HTML] Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based …
CE Cardenas, BM Beadle, AS Garden… - International Journal of …, 2021 - Elsevier
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 …
Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT
Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT)
treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop …
treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop …
[HTML][HTML] Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy
Abstract Background and Purpose Reducing trismus in radiotherapy for head and neck
cancer (HNC) is important. Automated deep learning (DL) segmentation and automated …
cancer (HNC) is important. Automated deep learning (DL) segmentation and automated …
Library of deep-learning image segmentation and outcomes model-implementations
An open-source library of implementations for deep-learning-based image segmentation
and outcomes models based on radiotherapy and radiomics is presented. As oncology …
and outcomes models based on radiotherapy and radiomics is presented. As oncology …
SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images
S Francis, G Pooloth, SBS Singam… - … Journal of Imaging …, 2023 - Wiley Online Library
Abstract The segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images
is an essential part of the planning phase of radiation treatment to avoid the adverse effects …
is an essential part of the planning phase of radiation treatment to avoid the adverse effects …
Deep learning-augmented head and neck organs at risk segmentation from CT volumes
Purpose: A novel deep learning model, Siamese Ensemble Boundary Network (SEB-Net)
was developed to improve the accuracy of automatic organs-at-risk (OARs) segmentation in …
was developed to improve the accuracy of automatic organs-at-risk (OARs) segmentation in …
Bi-Graph Reasoning for Masticatory Muscle Segmentation from Cone-Beam Computed Tomography
Automated segmentation of masticatory muscles is a challenging task considering
ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam …
ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam …
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
This paper presents a deep learning framework for medical video segmentation.
Convolution neural network (CNN) and transformer-based methods have achieved great …
Convolution neural network (CNN) and transformer-based methods have achieved great …
[HTML][HTML] Library of model implementations for sharing deep-learning image segmentation and outcomes models
An open-source library of implementations for deep-learning based image segmentation
and radiotherapy outcomes models is presented in this work. As oncology treatment …
and radiotherapy outcomes models is presented in this work. As oncology treatment …