Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Henderson, Edward G."'
Autor:
Huijben, Evi M. C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Park, Jong Sung, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A. H., Henderson, Edward G. A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density
Externí odkaz:
http://arxiv.org/abs/2403.08447
Autor:
Sargeant, Chelsea A. H., Henderson, Edward G. A., McSweeney, Dónal M., Rankin, Aaron G., Page, Denis
Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment cone-beam CTs (CBCTs) with a view to improving image quality and enabling accurate dose computation to facilitate a CBCT-based adaptive radiotherapy workflow. As this area of
Externí odkaz:
http://arxiv.org/abs/2312.02017
The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation in 3D shape
Externí odkaz:
http://arxiv.org/abs/2309.14269
Convolutional neural networks (CNNs) are increasingly being used to automate segmentation of organs-at-risk in radiotherapy. Since large sets of highly curated data are scarce, we investigated how much data is required to train accurate and robust he
Externí odkaz:
http://arxiv.org/abs/2303.17318
Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans
We present GAAF, a Generalised Automatic Anatomy Finder, for the identification of generic anatomical locations in 3D CT scans. GAAF is an end-to-end pipeline, with dedicated modules for data pre-processing, model training, and inference. At it's cor
Externí odkaz:
http://arxiv.org/abs/2209.06042
Abdominal organ segmentation is a difficult and time-consuming task. To reduce the burden on clinical experts, fully-automated methods are highly desirable. Current approaches are dominated by Convolutional Neural Networks (CNNs) however the computat
Externí odkaz:
http://arxiv.org/abs/2207.10446
Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to c
Externí odkaz:
http://arxiv.org/abs/2206.13317
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