Guidewire Endpoint Detection Based on Pixel-Adjacent Relation during Robot-Assisted Intravascular Catheterization: In Vivo Mammalian Models

Du，Wenjing1,2; Yi，Guanlin1; Omisore，Olatunji Mumini1; Duan，Wenke1,3; Chen，Xingyu1; Akinyemi，Toluwanimi1; Liu，Jiang4; Lee，Boon Giin2; Wang，Lei1

2024

10.1002/aisy.202300687

Advanced Intelligent Systems   影响因子和分区

2640-4567

Existing surgical guidewire endpoint localization methods in X-ray images face challenges owing to their small size, simple appearance, nonrigid nature of objects, low signal-to-noise ratio of X-ray images, and imbalance between the number of guidewire and background pixels, which lead to errors in surgical navigation. An eight-neighborhood-based method for increasing the localization accuracy of guidewire endpoint to improve the safety of interventional procedures is proposed herein. The proposed method includes two stages: 1) An improved U-Net network is employed for segmenting the data of the guidewire to extract regions of interest containing guidewire endpoints with higher precision and to reduce interference from other anatomical structures and imaging artifacts. 2) The proposed method detects guidewire endpoints using the adjacent relationship between pixels in the eight-neighborhood regions. This stage covers skeletonization extraction, removal of bifurcation points, and repair of fracture points. This study achieves mean pixel errors of 2.02 and 2.13 pixels in an in vivo rabbit and porcine X-ray fluoroscopy images, outperforming ten classic heatmap and regression methods, achieving state-of-the-art detection results. The proposed method can also be applied to detect other tiny surgical instruments such as stents and balloons, while preserving the flexibility of the guidewire bending angle.

eight neighborhoods endpoint detections guidewire endpoints semantic segmentations skeletonization extractions

SCI ; EI

英语

其他

National Natural Science Foundation of China["U21A20480","U191320006","61950410618"] ; Natural Science Foundation of China[2019YFB1311700] ; National Key Research and Development Plan[JCYJ20190812173205538] ; Shenzhen Natural Science Foundation[SIAT-IRB-220915-H0606] ; Shenzhen Advanced Animal Study Service Center[191204P]

Automation & Control Systems ; Computer Science ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Robotics

WOS:001137139200001

WILEY

2-s2.0-85181477312

Scopus

1.Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,1068 Xueyuan Avenue, Shenzhen University Town,518055,China
2.School of Computer Science,University of Nottingham Ningbo China,Ningbo,199 Taikang East Road,315100,China
3.Academy for Engineering and Technology,Fudan University,Shanghai,220 Handan Road,200433,China
4.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,1088 Xueyuan Blvd., Nanshan District,518055,China

Du，Wenjing,Yi，Guanlin,Omisore，Olatunji Mumini,et al. Guidewire Endpoint Detection Based on Pixel-Adjacent Relation during Robot-Assisted Intravascular Catheterization: In Vivo Mammalian Models[J]. Advanced Intelligent Systems,2024,6(4).

Du，Wenjing.,Yi，Guanlin.,Omisore，Olatunji Mumini.,Duan，Wenke.,Chen，Xingyu.,...&Wang，Lei.(2024).Guidewire Endpoint Detection Based on Pixel-Adjacent Relation during Robot-Assisted Intravascular Catheterization: In Vivo Mammalian Models.Advanced Intelligent Systems,6(4).

Du，Wenjing,et al."Guidewire Endpoint Detection Based on Pixel-Adjacent Relation during Robot-Assisted Intravascular Catheterization: In Vivo Mammalian Models".Advanced Intelligent Systems 6.4(2024).