MSLNet and Perceptual Grouping for Guidewire Segmentation and Localization

Area of Science:

• Medical Imaging
• Computer Vision
• Interventional Cardiology

Background:

• Fluoroscopy (real-time X-ray) is vital for guiding minimally invasive coronary angioplasty.
• Identifying the guidewire in noisy fluoroscopy images is challenging due to its thinness and low visibility.
• Accurate guidewire localization is essential for precise stent and balloon placement during vessel repair.

Purpose of the Study:

• To develop an effective method for guidewire localization in fluoroscopy images.
• To improve the accuracy and efficiency of guidewire segmentation and localization.
• To provide a robust solution for a critical step in minimally invasive cardiac procedures.

Main Methods:

• A novel guidewire segmentation method using a residual network (ResNet) for feature extraction.
• Prediction of coarse segmentation refined in promising areas for fine segmentation.
• A perceptual grouping method to merge initial pixel chain curves into a few representative curves.

Main Results:

• The proposed method achieves competitive results against established segmentation techniques like Res-UNet and nnU-Net.
• Demonstrated effectiveness on datasets with 871 and 23,449 frames.
• Achieved faster inference times without utilizing skip connections.

Conclusions:

• The developed method offers a promising approach for accurate and efficient guidewire localization.
• This technique can enhance the safety and efficacy of minimally invasive coronary angioplasty.
• The novel segmentation and perceptual grouping strategies contribute to advancing medical image analysis.