创伤性脑出血体积定量的深度学习方法研究

陈地友; 时心怡; 吴鹏飞; 詹力; 赵文兵; 谢静茹; 张良; 赵辉

doi:10.16016/j.2097-0927.202311033

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创伤性脑出血体积定量的深度学习方法研究

临床医学 | 更新时间：2026-01-09

- 创伤性脑出血体积定量的深度学习方法研究
- Deep learning for volumetric assessment of traumatic cerebral hematoma
- 陆军军医大学学报 2024年46卷第19期页码：2225-2235
- 作者机构：
  
  1.400042 重庆，陆军特色医学中心放射科
  2.400042 重庆，陆军特色医学中心军事交通伤防治研究室
  3.401135 重庆，重庆理工大学两江人工智能学院
- 作者简介：
  
  [赵辉，E-mail：box.zhaohui@163.com ZHAO Hui, E-mail: box.zhaohui@163.com
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金(CSTB2023NSCQ-LZX0155)
- DOI：10.16016/j.2097-0927.202311033
  中图分类号： R319;R445.2;R743.34
- 收稿：2023-11-07，
  
  修回：2024-05-10，
  
  纸质出版：2024-10-15
- 稿件说明：
移动端阅览
陈地友, 时心怡, 吴鹏飞, 等. 创伤性脑出血体积定量的深度学习方法研究[J]. 陆军军医大学学报, 2024,46(19):2225-2235.

Diyou CHEN, Xinyi SHI, Pengfei WU, et al. Deep learning for volumetric assessment of traumatic cerebral hematoma[J]. Journal of Army Medical University, 2024, 46(19): 2225-2235.
陈地友, 时心怡, 吴鹏飞, 等. 创伤性脑出血体积定量的深度学习方法研究[J]. 陆军军医大学学报, 2024,46(19):2225-2235. DOI： 10.16016/j.2097-0927.202311033.

Diyou CHEN, Xinyi SHI, Pengfei WU, et al. Deep learning for volumetric assessment of traumatic cerebral hematoma[J]. Journal of Army Medical University, 2024, 46(19): 2225-2235. DOI： 10.16016/j.2097-0927.202311033.

摘要

目的

基于Trans-UNet模型构建创伤性脑出血(traumatic intracerebral hemorrhage

TICH)体积定量的深度学习方法，并与公式法进行比较。

方法

收集我院放射科于2018年5月至2023年5月收治的141例创伤性脑出血患者的CT资料，建立基于Trans-UNet模型的深度学习方法，以影像归档与通讯系统(picture archiving and communication system

PACS)人工勾画为金标准，对比其与公式法(共10种)在创伤性脑出血体积定量的准确性、一致性和测量耗时差异。

结果

PACS人工勾画的创伤性脑出血中位体积为1.167 mL，单个患者的测量中位时间为135 s。深度学习方法与PACS人工勾画的体积百分误差中位数为3.59%，Spearman相关系数为0.999(

＜0.001)，单个患者测量的中位时间仅为4.38 s；而公式法中，最低体积百分误差中位数为16.451%，最高Spearman相关系数为0.986(

＜0.001)，单个患者测量的最低中位时间为20 s。两类方法在体积百分误差和测量时间上的差异均具有统计学意义(

均＜0.001)。

结论

本研究所构建的深度学习方法相较于公式法，在测量准确性和时效上均表现更优。

Abstract

Objective

To develop a deep learning method for volumetric assessment of traumatic intracerebral hemorrhage (TICH) using the Trans-UNet model and to compare its performance with traditional formula-based methods.

Methods

CT data from 141 TICH patients admitted to Army Medical Center of PLA between May 2018 and May 2023 were collected. A deep learning method based on the Trans-UNet model was established. Manual delineation

via

picture archiving and communication system (PACS) was served as the gold standard for comparing the accuracy

consistency

and time efficiency of our method against 10 different formula-based methods for measuring the amount of TICH.

Results

The median volume of TICH

as manual delineation

via

PACS

was 1.167 mL

with a median measurement time of 135 s per patient. The median percentage error in volume between the deep learning method and manual delineation

via

PACS was 3.59%. Spearman correlation coefficient was 0.999 (

0.001)

and a median measurement time was only 4.38 s per patient. In contrast

in the formula-based methods

the lowest median percentage error in volume was 16.451%

the highest Spearman correlation coefficient was 0.986 (

0.001)

and the lowest median measurement time was 20 s for a single patient. The statistical differences were observed in percentage error in volume and measurement time between the 2 types of methods (all

0.001).

Conclusion

Our developed deep learning method for volumetric assessment of TICH is superior to the formula-based methods in terms of measurement accuracy and time efficiency.

关键词

Keywords

references

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