| 注册
首页|期刊导航|刑事技术|基于深度学习语义分割技术的气胸肺萎陷程度自动化计算

基于深度学习语义分割技术的气胸肺萎陷程度自动化计算

罗帅 刘安杰 张兴涛 占梦军 刘猛 范飞 周宇驰 刘长远 邓振华

刑事技术2024,Vol.49Issue(5):472-479,8.
刑事技术2024,Vol.49Issue(5):472-479,8.DOI:10.16467/j.1008-3650.2024.0035

基于深度学习语义分割技术的气胸肺萎陷程度自动化计算

Research on Calculation of Lung Compression Degree in Pneumothorax Using Semantic Segmentation Based on Deep Learning

罗帅 1刘安杰 2张兴涛 3占梦军 3刘猛 4范飞 3周宇驰 3刘长远 5邓振华3

作者信息

  • 1. 四川大学华西基础医学与法医学院,成都 610041||四川大学计算机学院,成都 610041
  • 2. 电子科技大学计算机科学与工程学院,成都 611731
  • 3. 四川大学华西基础医学与法医学院,成都 610041
  • 4. 四川大学计算机学院,成都 610041
  • 5. 宜宾鑫正司法鉴定所,四川 宜宾 644022
  • 折叠

摘要

Abstract

Calculation of the degree of lung compression by Mimics software remains the"gold standard".In the forensic sphere,due to the complexity of the Mimics software,many people do not utilize this method in forensic practice.They may calculate degree of lung compression by visual observation,represent the result of degree of lung compression by some slicer of CT.These factors will lead to inaccuracies of calculated results.The aim of this study is to develop a model for automatic calculation of lung compression degree based on deep learning semantic segmentation technology,and explore the feasibility of deep learning for lung compression measurement by comparing the results of automatic calculations with Mimics software.In this study,42 cases of the computed tomography(CT)data including pneumothorax diagnosis in DICOM format were collected each cases has about 350 images with a thickness of 1 mm.Among them,32 cases used for training and 10 cases used for validation.The air-containing regions of 1943 images were manually annotated.An additional five chest CT cases were selected for external testing.The degree of lung compression was calculated by both the deep learning model and Mimics software,and the correlation between the results of the two methods and the calculation errors were analyzed.In the validation set,the average error between the deep learning model calculation results and the manual method was 2.4%,and the model processed an average of 356 per case with an average time of 60.04 s,while the average error in the test set was 4.4%.The aforementioned results lead to the following conclusions:The deep learning model constructed in this study has the potential to be applied in the automated measurement of the lung compression degree due to pneumothorax,which can provide a reference for the calculation of the lung compression degree due to pneumothorax in forensic practice.

关键词

法医临床学/深度学习/Mimics软件/肺萎陷/三维重建

Key words

forensic clinical medicine/deep learning/Mimics software/lung compression/three-dimensional reconstruction

分类

社会科学

引用本文复制引用

罗帅,刘安杰,张兴涛,占梦军,刘猛,范飞,周宇驰,刘长远,邓振华..基于深度学习语义分割技术的气胸肺萎陷程度自动化计算[J].刑事技术,2024,49(5):472-479,8.

基金项目

四川省自然科学基金项目青年基金项目(24NSFSC6731) (24NSFSC6731)

上海市现场物证重点实验室开放课题(2023XCWZK03) (2023XCWZK03)

刑事技术

OACSTPCD

1008-3650

访问量5
|
下载量0
段落导航相关论文