MSLNet and Perceptual Grouping for Guidewire Segmentation and Localization

Fluoroscopy (real-time X-Ray) images are used for monitoring minimally-invasive coronary angioplasty operations such as stent placement. During these operations a thin wire called a guidewire is used to guide different tools such as the stent or a balloon in order to repair the vessels. However, fluoroscopy images are noisy and the guidewire is very thin, practically invisible in many places, making its localization very difficult. Guidewire segmentation is the task of finding the guidewire pixels, while guidewire localization is the higher level task aimed at finding a parameterized curve describing the guidewire points. This paper presents a method for guidewire localization that starts from a guidewire segmentation, from which it extracts a number of initial curves as pixel chains and uses a novel perceptual grouping method to merge these initial curves into a small number of curves. The paper also introduces a novel guidewire segmentation method that uses a residual network (ResNet) as a feature extractor and predicts a coarse segmentation that is refined only in promising locations to a fine segmentation. Experiments on two challenging datasets show that the method outperforms existing segmentation methods such as Res-UNet and nnU-Net, while having no skip connections and a faster inference time.