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.

我们引入了一种对导管和导丝进行形状敏感的损失函数，并将其应用于视觉转换网络中，在大规模 X 射线图像数据集上取得了最新的研究成果。我们将网络预测和相应的真值转换为有符号距离图，从而使任何网络能够集中精力处理关键的边界而不仅限于整体轮廓。通过计算这些特征向量之间的余弦相似度，我们对图像相似性有了更细致的理解，超越了传统基于重叠度的测量方法的局限。我们的方法具有许多优势，包括尺度和平移不变性，以及对微小差异的较好检测，从而确保图像中医疗仪器的准确定位和描述。全面的定量和定性分析证实了相比现有基准的显著性能提升，展示了我们新的形状敏感损失函数在改善导管和导丝分割方面的潜力。