| 基于2D/3D U-plus-net的心脏自动分割 |
| |
| 引用本文： | 宋宇宸,彭昭,吴昊天,周解平,皮一飞,陈志,裴曦.基于2D/3D U-plus-net的心脏自动分割[J].中国医学物理学杂志,2021,0(9):1172-1178. |
| |
| 作者姓名： | 宋宇宸 彭昭 吴昊天 周解平 皮一飞 陈志 裴曦 |
| |
| 作者单位： | 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 |
| |
| 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。
|
| 关 键 词： | 心脏 AlexNet 2D U-plus-net 3D U-plus-net 自动分割 |
Automatic heart segmentation based on 2D/3D U-plus-net |
| |
| SONG Yuchen,PENG Zhao,WU Haotian,ZHOU Jieping,PI Yifei,CHEN Zhi,PEI Xi.Automatic heart segmentation based on 2D/3D U-plus-net[J].Chinese Journal of Medical Physics,2021,0(9):1172-1178. |
| |
| Authors: | SONG Yuchen PENG Zhao WU Haotian ZHOU Jieping PI Yifei CHEN Zhi PEI Xi |
| |
| Affiliation: | 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 |
| |
| 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. |
| |
| Keywords: | heartAlexNet 2D U-plus-net 3D U-plus-net automatic segmentation |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《中国医学物理学杂志》浏览原始摘要信息 |
|
点击此处可从《中国医学物理学杂志》下载全文 |
|
