[1]宋宇宸,彭昭,吴昊天,等.基于2D/3D U-plus-net的心脏自动分割[J].中国医学物理学杂志,2021,38(9):1172-1178.[doi:10.3969/j.issn.1005-202X.2021.09.023]
　SONG Yuchen,PENG Zhao,WU Haotian,et al.Automatic heart segmentation based on 2D/3D U-plus-net[J].Chinese Journal of Medical Physics,2021,38(9):1172-1178.[doi:10.3969/j.issn.1005-202X.2021.09.023]
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基于2D/3D U-plus-net的心脏自动分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第9期
页码:
1172-1178
栏目:
医学人工智能
出版日期:
2021-09-26

文章信息/Info

Title:
Automatic heart segmentation based on 2D/3D U-plus-net
文章编号:
1005-202X（2021）09-1172-07
作者:
宋宇宸1; 彭昭1; 吴昊天2; 周解平3; 皮一飞4; 陈志1; 裴曦1
1. 中国科学技术大学放射医学物理中心，安徽合肥230027；2. 安徽慧软科技有限公司，安徽合肥230000；3. 中国科学技术大学 附属第一医院放疗科，安徽合肥230001；4. 郑州大学第一附属医院放疗科，河南郑州450052
Author(s):
SONG Yuchen1;  PENG Zhao1;  WU Haotian2;  ZHOU Jieping3;  PI Yifei4;  CHEN Zhi1;  PEI Xi1
1. Center of Radiological Medical Physics, University of Science and Technology of China, Hefei 230027, China 2. Anhui Wisdom Technology Co., Ltd, Hefei 230000, China 3. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China 4. Department of Radiation Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
关键词:
心脏; AlexNet; 2D U-plus-net; 3D U-plus-net; 自动分割
Keywords:
heartAlexNet 2D U-plus-net 3D U-plus-net automatic segmentation
分类号:
R318 R445
DOI:
10.3969/j.issn.1005-202X.2021.09.023
文献标志码:
A
摘要:
目的：利用2D/3D U-plus-net 提高心脏自动分割的准确率。方法：收集郑州大学第一附属医院60 例患者胸部扫 描CT图像（数据A）及中国科学技术大学附属第一医院45 例患者胸部扫描CT图像（数据B）。基于改进的AlexNet 将 CT 图像分为两类：心脏CT 图像和无心脏CT 图像。在2D/3D U-net 拓扑结构基础上，通过减小网络深度、在长连接中 增加新节点、增加解码器中卷积次数的方法，得到改进后的2D/3D U-plus-net；将靠近腹部的心脏CT图像（图像张数由 预实验决定）输入3D U-plus-net，其余图像输入2D U-plus-net；采用5 倍交叉验证法对模型进行训练及测试。最后通过 Dice 系数、HD95 和平均表面距离（MSD）评估自动分割精度。结果：数据A自动分割的Dice 系数为0.941±0.012，MSD 为（3.918±0.201）mm，HD95为（5.863±0.561）mm；数据B自动分割的Dice系数为0.934±0.014，MSD为（4.112±0.320）mm， HD95 为（6.035±0.659）mm。结论：基于2D/3D U-plus-net 的分割方法提高了心脏自动分割准确率。
Abstract:
Objective To improve the accuracy of automatic heart segmentation using 2D/3D U-plus-net. Methods The chest CT images of 60 patients from the First Affiliated Hospital of Zhengzhou University (Data A) and the chest CT images of 45 patients from the First Affiliated Hospital of University of Science and Technology of China (Data B) were collected. A modified AlexNet was used to divide all CT images into two types, namely heart CT images and no-heart CT images. Based on the topological structure of 2D/3D U-net, a modified 2D/3D U-plus-net was obtained by reducing network depth, increasing nodes in a long connection and increasing the convolution number of the decoder. The heart CT images near the abdomen (the number of CT images was determined by pre-experiment) were input into 3DU-plus-net, while the other heart CT images were input into 2D U-plus-net. The obtained model was trained and tested by 5-fold cross-validation method. Finally, the accuracy of automatic heart segmentation was evaluated by Dice coefficient, HD95 and mean surface distance. Results The Dice coefficient, mean surface distance and HD95 of automatic heart segmentation on Data A were 0.941± 0.012, (3.918±0.201) mm and (5.863±0.561) mm, respectively, while those of automatic heart segmentation on Data B were 0.934±0.014, (4.112±0.320) mm and (6.035±0.659) mm, respectively. Conclusion The automatic segmentation method based on 2D/3D U-plus-net improves the accuracy of automatic heart segmentation.

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备注/Memo

备注/Memo:
【收稿日期】2021-03-02 【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】宋宇宸，硕士，研究方向：人工智能在医学影像与放射治 疗中的应用，E-mail: songycyc@mail.ustc.edu.cn 【通信作者】裴曦，博士，副教授，研究方向：医学影像和放疗物理、辐 射探测和辐射防护剂量学以及蒙特卡罗计算方法在核科 学技术上的应用，E-mail: xpei@ustc.edu.cn
更新日期/Last Update: 2021-09-27