  We introduce a shape-sensitive loss function for catheter and guidewire
segmentation and utilize it in a vision transformer network to establish a new
state-of-the-art result on a large-scale X-ray images dataset. We transform
network-derived predictions and their corresponding ground truths into signed
distance maps, thereby enabling any networks to concentrate on the essential
boundaries rather than merely the overall contours. These SDMs are subjected to
the vision transformer, efficiently producing high-dimensional feature vectors
encapsulating critical image attributes. By computing the cosine similarity
between these feature vectors, we gain a nuanced understanding of image
similarity that goes beyond the limitations of traditional overlap-based
measures. The advantages of our approach are manifold, ranging from scale and
translation invariance to superior detection of subtle differences, thus
ensuring precise localization and delineation of the medical instruments within
the images. Comprehensive quantitative and qualitative analyses substantiate
the significant enhancement in performance over existing baselines,
demonstrating the promise held by our new shape-sensitive loss function for
improving catheter and guidewire segmentation.

Image and Video Processing;Computer Vision and Pattern Recognition