基于组合网络的图像头颈放疗危及器官自动分割(4) - 中国耳鼻咽喉头颈外科杂志社投稿_期刊论文发表|版面费|电话|编辑部- 中国耳鼻咽喉头颈外科

一、本刊要求作者有严谨的学风和朴实的文风，提倡互相尊重和自由讨论。凡采用他人学说，必须加注说明。二、不要超过10000字为宜，精粹的短篇，尤为欢迎。三、请作者将稿件（用WORD格式）发送到下面给出的征文信箱中。四、凡来稿请作者自留底稿，恕不退稿。五、为规范排版，请作者在上传修改稿时严格按以下要求： 1．论文要求有题名、摘要、关键词、作者姓名、作者工作单位（名称，省市邮编）等内容一份。 2．基金项目和作者简介按下列格式：基金项目：项目名称（编号）作者简介：姓名（出生年－），性别，民族（汉族可省略），籍贯，职称，学位，研究方向。 3．文章一般有引言部分和正文部分，正文部分用阿拉伯数字分级编号法，一般用两级。插图下方应注明图序和图名。表格应采用三线表，表格上方应注明表序和表名。 4．参考文献列出的一般应限于作者直接阅读过的、最主要的、发表在正式出版物上的文献。其他相关注释可用脚注在当页标注。参考文献的著录应执行国家标准GB7714-87的规定，采用顺序编码制。

基于组合网络的图像头颈放疗危及器官自动分割(4)

作者:

关键词:

摘要：

[9] Ibragimov B, Xing L. Segmentation of organsat-risks in head and neck CT images using convolutional neural networks [J]. Medical Physics, 2017, 44(2): 547-557.

[10] Lei Y, Fu YB, Wang TH, et al. Deep learning in multi-organ segmentation [Z/OL]. arXiv Preprint, arXiv: 2001., 2020.

[11] Zhu WT, Huang YF, Tang H, et al. Anatomynet: deep 3D squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation [Z/OL]. arXiv Preprint, arXiv:, 2018.

[12] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C] // The IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.

[13] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation [C] // The Medical Image Computing and Computer Assisted Intervention, 2015: 234-241.

[14] Kamnitsas K, Ledig L, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation [J]. Medical Image Analysis, 2017, 36: 61-78.

[15] Zhou YY, Xie LX, Shen W, et al. A fixed-point model for pancreas segmentation in abdominal CT scans [C] // The Medical Image Computing and Computer Assisted Intervention, 2017: 693-701.

[16] Han X. Automatic liver lesion segmentation using a deep convolutional neural network method [Z/OL]. arXiv Preprint, arXiv:, 2017.

[17] Li XM, Chen H, Qi XJ, et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes [J]. IEEE Transactions on Medical Imaging, 2018, 37(12): 2663-2674.

[18] Isensee F, Petersen J, Kohl SAA, et al. nnU-Net: breaking the spell on successful medical image segmentation [Z/OL]. arXiv Preprint, arXiv: , 2019.

[19] Avants BB, Epstein CL, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain [J]. Medical Image Analysis, 2008, 12(1): 26-41.

贺宝春, 贾富仓. 基于组合 U-Net 网络的 CT 图像头颈放疗危及器官自动分割 [J]. 集成技术, 2020, 9(2): 17-24.He BC, Jia FC. Head and neck CT segmentation based on a combined U-Net model [J]. Journal of Integration Technology, 2020, 9(2): 17-24.

文章来源：《中国耳鼻咽喉头颈外科》网址: http://www.zgebyhtgwk.cn/qikandaodu/2021/0708/570.html