Title:Accurate Recognition of Vascular Lumen Region from 2D Ultrasound Cine Loops for Bubble Detection
Volume: 20
Author(s): Ziyi Wang, Zhuochang Yang, Ziye Chen, Xiaoyu Huang, Lifan Xu, Chang Zhou, Yingjie Zhou, Baoliang Zhu, Kun Zhang, Deren Gong, Weigang Xu and Jiangang Chen*
Affiliation:
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
Keywords:
Ultrasound videos, KCF, Tissue segmentation, Bubble detection, Automatic algorithm, DCS.
Abstract:
Background:
Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the
detection of vascular gas emboli for the diagnosis of decompression sickness.
Objective:
To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound
videos with the interference of bubble presence.
Methods:
This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel
lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with
circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique
were adopted in this algorithm.
Results:
A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble
videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different
bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82.
Conclusion:
It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the
measurement of the severity of vascular gas emboli in clinics.