A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

机译：基于卷积神经网络的直肠癌定位定向对分割精度的影响研究

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PurposeConvolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN.

机译：目的卷积神经网络（CNN）极大地改善了医学图像分割。健壮的模型要求训练数据可以代表整个数据集。不同的特征之一来自于放疗患者位置（俯卧或仰卧）的变化。在这项研究中，我们调查了位置取向对使用CNN分割的影响。

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期刊名称 Journal of Applied Clinical Medical Physics
作者
Kuo Men; Pamela Boimel; James Janopaul‐Naylor; Chingyun Cheng; Haoyu Zhong; Mi Huang; Huaizhi Geng; Yong Fan; John P. Plastaras; Edgar Ben‐Josef; Ying Xiao;
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作者单位

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年(卷),期 2019(20),1
年度 2019
页码 110–117
总页数 8
原文格式 PDF
正文语种
中图分类放射医学;影像诊断学;
关键词
convolutional neural networks deep learning positioning orientation rectal cancer radiotherapy segmentation;

机译：卷积神经网络;深度学习;定位方向;直肠癌放疗;分割;

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1. Automatic segmentation of the clinical target volume and organs at risk in the planning CT CT for rectal cancer using deep dilated convolutional neural networks [J] . Men Kuo, Dai Jianrong, Li Yexiong Medical Physics . 2017,第12期

机译：使用深扩张的卷积神经网络的直肠癌患者CT CT的临床目标体积和器官的自动分割
2. Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network [J] . Kim Jongin, Oh Ji Eun, Lee Joohyung, International journal of imaging systems and technology . 2019,第3期

机译：直肠癌：使用深度卷积神经网络实现T2和T3直肠癌的全自动鉴别
3. Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network [J] . Kim Jongin, Oh Ji Eun, Lee Joohyung, International journal of imaging systems and technology . 2019,第3期

机译：直肠癌：利用深卷积神经网络全自动地辨别T2和T3直肠癌
4. Deep Convolutional Neural Networks For Imaging Data Based Survival Analysis Of Rectal Cancer [C] . Hongming Li, Pamela Boimel, James Janopaul-Naylor, IEEE International Symposium on Biomedical Imaging . 2019

机译：深度卷积神经网络用于基于图像数据的直肠癌生存分析
5. Cell segmentation in cancer histopathology images using convolutional neural networks. [D] . Kavassery Rajalingam, Viswanathan. 2016

机译：使用卷积神经网络在癌症组织病理学图像中进行细胞分割。
6. Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study [O] . Kenny H. Cha, Lubomir M. Hadjiiski, Ravi K. Samala, 2016

机译：CT中的膀胱癌分割用于治疗反应评估：深度学习卷积神经网络的应用-初步研究
7. Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration [O] . Nooshin Ghavami, Yipeng Hu, Eli Gibson, 2019

机译：使用卷积神经网络自动分割前列腺MRI：研究网络架构对体积测量准确性的影响和MRI超声登记

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

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