Deep learning for volumetric assessment of traumatic cerebral hematoma
10.16016/j.2097-0927.202311033
- VernacularTitle:创伤性脑出血体积定量的深度学习方法研究
- Author:
Diyou CHEN
1
,
2
;
Xinyi SHI
;
Pengfei WU
;
Li ZHAN
;
Wenbing ZHAO
;
Jingru XIE
;
Liang ZHANG
;
Hui ZHAO
Author Information
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
- Keywords:
traumatic intracerebral hemorrhage;
computed tomography;
deep learning;
volumetric quantification;
traumatic brain injury
- From:
Journal of Army Medical University
2024;46(19):2225-2235
- CountryChina
- Language:Chinese
-
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(P＜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(P＜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 P＜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.
