scholarly journals A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy

2021 ◽  
Author(s):  
Yijun Wu ◽  
Kai Kang ◽  
Chang Han ◽  
Shaobin Wang ◽  
Qi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Rectal Cancer ◽  
Convolutional Neural Network ◽  
Cancer Patients ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Neoadjuvant Radiotherapy

scholarly journals CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongjian Ju ◽  
Wen Guo ◽  
Shanshan Gu ◽  
Jin Zhou ◽  
Wei Yang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Ct Images ◽  
Clinical Samples ◽  
Model Parameters ◽  
Convolutional Network ◽  
Volume Delineation ◽  
Fully Connected

Abstract Background It is very important to accurately delineate the CTV on the patient’s three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. Methods In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. Results The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. Conclusions Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.

Definition and delineation of the clinical target volume for rectal cancer

2006 ◽  
Vol 65 (4) ◽  
pp. 1129-1142 ◽  
Author(s):  
Sarah Roels ◽  
Wim Duthoy ◽  
Karin Haustermans ◽  
Freddy Penninckx ◽  
Vincent Vandecaveye ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Clinical Target Volume ◽  
Target Volume

scholarly journals PV-0121: Falcon based Clinical Target Volume Delineation to support Inter-Society Rectal Cancer Guidelines

2016 ◽  
Vol 119 ◽  
pp. S55
Author(s):  
M. Gambacorta ◽  
G. Chiloiro ◽  
P. Das ◽  
K. Haustermans ◽  
I. Joye ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Target Volume Delineation ◽  
Volume Delineation ◽  
Cancer Guidelines
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