YOLOX target detection model for automatically identifying endovascular interventional instruments on images of digital subtract angiography
10.13929/j.issn.1672-8475.2024.02.008
- VernacularTitle:YOLOX目标检测模型用于自动识别数字减影血管造影图中的血管腔内介入器械
- Author:
Rui FENG
1
;
Hao FENG
;
Chao SONG
;
Shibo XIA
;
Qingsheng LU
Author Information
1. 上海理工大学健康科学与工程学院,上海 200093
- Keywords:
angiography,digital subtraction;
surgical instruments;
deep learning;
automatic recognition
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(2):100-104
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of a YOLOX target detection model for automatically identifying endovascular interventional instruments on images of digital subtract angiography(DSA).Methods DSA data of 37 patients who underwent abdominal endovascular interventional therapy were retrospectively analyzed.Totally 4 435 DSA images were captured and taken as data set,which were divided into training set(n=3 991)and verification set(n=444)at the ratio of 9∶1.Six kinds of endovascular interventional instruments were labeled.YOLOX algorithm was applied for deep learning of data in training set in order to build a target detection model,and the efficacy of the model for automatically identifying endovascular interventional instruments on DSA images was evaluated based on varification set.Results A total of 6 668 labels were put on 4 435 DSA images,aimed on Terumo 0.035in loach guide wire(n=587),Cook Lunderquist super hard guide wire(n=990),Optimed 5F with graduated pig tail catheter(n=1 680),Cordis MPA multi-functional catheter(n=667),Boston Scientific V-18 controllable guide wire(n=1 330)and Terumo 6F long sheath(n= 1 414),respectively.The training set contained 527,875,1 466,598,1 185 and 1 282,while the verification set contained 60,115,214,69,145 and 132 the above labels,respectively.The pixel accuracy of YOLOX target detection model for automatically identifying the above instruments in the verification set was 95.23%,97.32%,99.18%,98.97%,97.60%and 98.19%,respectively,with a mean pixel accuracy of 97.75%.Conclusion YOLOX target detection model could automatically identify endovascular interventional instruments on images of DSA.
