[1]蔡凯雄,王强,陈添峰,等.胎儿大脑三维表面重建算法[J].华侨大学学报(自然科学版),2024,45(1):78-85.[doi:10.11830/ISSN.1000-5013.202306012]
　CAI Kaixiong,WANG Qiang,CHEN Tianfeng,et al.Fetal Brain Three-Dimensional Surface Reconstruction Algorithm[J].Journal of Huaqiao University(Natural Science),2024,45(1):78-85.[doi:10.11830/ISSN.1000-5013.202306012]
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胎儿大脑三维表面重建算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第45卷
期数:
2024年第1期
页码:
78-85
栏目:
出版日期:
2024-01-11

文章信息/Info

Title:
Fetal Brain Three-Dimensional Surface Reconstruction Algorithm
文章编号:
1000-5013(2024)01-0078-08
作者:
蔡凯雄1;  王强2;  陈添峰2;  郑力新1
1. 华侨大学 工学院, 福建 泉州 362021;2. 泉州市妇幼保健院 儿童医院, 福建 泉州 362000
Author(s):
CAI Kaixiong1;  WANG Qiang2;  CHEN Tianfeng2;  ZHENG Lixin1
1. College of Engineering, University of Huaqiao, Quanzhou 362021, China; 2. Children’s Hospital, Quanzhou Maternal and Child Health Hospital, Quanzhou 362000, China
关键词:
胎儿大脑;  三维重建;  边缘重构;  点云处理;  核磁共振
Keywords:
fetal brain;  three-dimensional reconstruction;  edge reconstruction;  point cloud processing;  nuclear magnetic resonance
分类号:
TP399
DOI:
10.11830/ISSN.1000-5013.202306012
文献标志码:
A
摘要:
通过核磁共振设备获得多个离散间距的磁共振切片图像,采用CARESU_NET卷积神经网络对图像进行分割,获取胎儿大脑区域图像。采用CARESU_NET卷积神经网络对间断切片进行边缘重构,恢复完整的边缘信息。对边缘重构后的图像组提取边缘像素,生成三维点云,运用泊松重建方法重建点云表面,得到胎儿大脑三维表面模型。结果表明:基于核磁共振图像的三维表面模型直观生动,提高诊断效率和准确性。
Abstract:
Multiple discrete space magnetic resonance slice images are obtained using a nuclear magnetic resonance device,a CARESU_NET convolutional neural network is used to segment image to extract the fetal brain region images. A CARESU_NET convolutional neural network is used to reconstruct edge on discontinuous slices,complete edge information is restored. A three-dimensional point clouds are generated by extracting edge pixels from the edge-reconstructed images, and the point cloud surface is reconstructed using the Poisson reconstruction method to obtain a three-dimensional surface model of the fetal brain. The results show that the three-dimensional surface model based on nuclear magnetic resonance images is intuitive and vivid, the diagnostic efficiency and accuracy are improved.

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备注/Memo

备注/Memo:
收稿日期: 2023-06-12
通信作者: 郑力新(1967-),男,教授,博士,主要从事图像分析、机器视觉、深度学习方法、大数据分析、机器人与视觉一体化技术、网络控制、机电一体化系统等的研究。E-mail:zlx@hqu.edu.cn。
基金项目: 福建省科技计划项目(2020Y0039); 福建省华侨大学院校联合创新项目(2022YX008)
更新日期/Last Update: 2024-01-20