Interventional guidewire segmentation algorithm based on coded deep neural networks

TACE is the main non-surgical method for the treatment of intermediate and advanced liver cancer, and during the surgery, the surgeon determines the guidewire position by DSA image guidance. Due to the thin guidewire and complex image background, in this paper, GWnet network is proposed to enhance the guidewire segmentation accuracy. The network first facilitates the model's extraction of elongated tubular features of the guidewire by replacing the conventional convolution with a strip pooled pyramidal dispersed attention module (SPSA); secondly, hollow space convolutional pooling pyramid module (IASPP), which incorporates the inverse bottleneck layer structure, is embedded into the network to expanding the model sensing area while reducing feature loss.; Ultimately, by introducing a coordinated attention mechanism to reduce the effect of background noise and improve the resolution of the model to recognize guidewire targets and their edge details. Experimental results on 4839 anonymized DSA contrast images show that the guidewire segmentation method achieves good results.