[1]孟祥银,吴香奕,彭昭,等.结合伪MRI信息的腹部CT危及器官自动勾画[J].中国医学物理学杂志,2022,39(2):203-208.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.013]
　MENG Xiangyin,WU Xiangyi,PENG Zhao,et al.Auto-segmentation of organs-at-risk in abdominal CT after combining with synthetic-MRI information[J].Chinese Journal of Medical Physics,2022,39(2):203-208.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.013]
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结合伪MRI信息的腹部CT危及器官自动勾画()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第2期
页码:
203-208
栏目:
医学影像物理
出版日期:
2022-02-26

文章信息/Info

Title:
Auto-segmentation of organs-at-risk in abdominal CT after combining with synthetic-MRI information
文章编号:
1005-202X（2022）02-0203-06
作者:
孟祥银1; 吴香奕1; 彭昭1; 徐榭1; 2; 裴曦1; 2
1.中国科学技术大学核科学技术学院， 安徽 合肥 230026； 2.安徽慧软科技有限公司， 安徽 合肥 230088
Author(s):
MENG Xiangyin1;  WU Xiangyi1;  PENG Zhao1;  XU Xie1;  2;  PEI Xi 1;  2
1. School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230026, China 2. Anhui Wisdom Technology Co., Ltd, Hefei 230088, China
关键词:
伪MRI; CycleGAN; 腹部; 自动勾画
Keywords:
Keywords: synthetic-magnetic resonance imaging CycleGAN abdomen auto-segmentation
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2022.02.013
文献标志码:
A
摘要:
目的：结合伪MRI（sMRI）软组织信息，提出新的腹部器官自动勾画模型，改进CT软组织的勾画效果。方法：使用两个独立的深度神经网络分步完成病人腹部危及器官的自动勾画。首先，基于CycleGAN网络构建由CT图像转换sMRI图像的模型，采用去噪判别器等改进方法，得到器官轮廓一致的高清晰度sMRI。其次，使用sMRI与手工勾画信息训练自动勾画模型Residual U-Net，在CT和sMRI上分别自动勾画危及器官轮廓，Residual U-Net的残差模块能够充分利用提取到的特征来区分不同的器官。采用戴斯相似性系数（DSC）作为自动勾画模型分割精度的评价标准，35例宫颈癌与35例前列腺癌患者用于自动勾画模型的训练和评估。结果：结合sMRI信息的自动勾画模型在直肠、膀胱、左右股骨头的平均DSC分别为0.779±0.021、0.944±0.006、0.834±0.006、0.845±0.021。结论：使用结合sMRI信息的腹部CT自动勾画方法，可以在直肠获得更精确的自动勾画结果。
Abstract:
Abstract: Objective To propose an abdominal multi-organ auto-segmentation model in combination with synthetic-MRI (sMRI) soft tissue information, thereby improving the performance of CT soft tissue segmentation. Methods Two independent deep neural networks were used to complete the auto-segmentations of abdominal organs-at-risk step by step. A model for converting CT images into sMRI images was constructed based on CycleGAN network, and a high-definition sMRI with consistent organ contours was obtained by an improved denoising discriminator. Then, auto-segmentation model Residual U-Net was trained by sMRI and manual segmentation information to obtain the contours of organs-at-risk in CT and sMRI. The residual module in Residual U-Net made full use of the extracted features to distinguish different organs. Dice similarity coefficient (DSC) was used as the evaluation criterion for the segmentation accuracy of the auto-segmentation model. A total of 35 patients with cervical cancer and 35 patients with prostate cancer were used for training and evaluation of the model. Results The average DSC of the auto-segmentation model for the rectum, bladder and left and right femoral heads was 0.779±0.021, 0.944±0.006, 0.834±0.006 and 0.845±0.021, respectively. Conclusion The abdominal CT auto-segmentation method which combines with sMRI information can obtain more accurate results in the rectum segmentation.

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[1]尹智贤,夏克文,张昭,等.融合上下文注意力的两段式生成对抗网络的肺结节图像生成与分类[J].中国医学物理学杂志,2024,41(12):1517.[doi:DOI:10.3969/j.issn.1005-202X.2024.12.009]
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备注/Memo

备注/Memo:
【收稿日期】2021-07-19 【基金项目】安徽省自然科学基金（1908085MA27）；安徽省重点研究与开发计划（1804a09020039） 【作者简介】孟祥银，硕士，主要从事深度学习、图像生成与分割等研究，E-mail: mittym@mail.ustc.edu.cn 【通信作者】裴曦，博士，副教授，主要从事医学物理、人工智能和医学影像等研究，E-mail: xpei@ustc.edu.cn
更新日期/Last Update: 2022-03-07