METHODS: The research presented couples thermographic examination during DIEP flap breast reconstruction with automatic segmentation approach using a convolutional neural network. Traditional segmentation techniques and annotations by surgeons are used to create automatic labels for the training.
RESULTS: The network used for image annotation is able to label in real-time on minimal hardware and the labels created can be used to locate and quantify perforator candidates for selection with a dice score accuracy of 0.8 after 2 min and 0.9 after 4 min.
CONCLUSIONS: These results allow for a computational system that can be used in place during surgery to improve surgical success. The ability to track and measure perforators and their perfused area allows for less subjective results and helps the surgeon to select the most suitable perforator for DIEP flap breast reconstruction.
方法:该研究提出了将DIEP皮瓣乳房重建过程中的热成像检查与使用卷积神经网络的自动分割方法相结合。传统的分割技术和外科医生的注释用于创建用于训练的自动标签。
结果:用于图像注释的网络能够在最少的硬件上实时标记,并且创建的标签可用于定位和量化穿孔器候选,以在2分钟后获得0.8的骰子得分准确度,在4分钟后获得0.9的骰子得分准确度。
结论:这些结果允许一种计算系统,该系统可以在手术期间就地使用,以提高手术成功率。跟踪和测量穿孔器及其灌注面积的能力允许较少的主观结果,并帮助外科医生为DIEP皮瓣乳房重建选择最合适的穿孔器。