Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. the algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and daistolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.