Abstract：
OBJECTIVE: Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery. Dynamic infrared thermography (DIRT) can offer a solution for these disadvantages.
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.
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.
摘要：
目的：灌注不足是乳房切除术后乳房再造术组织转移中部分皮瓣丢失的最常见原因。当前最先进的技术使用计算机断层扫描血管造影（CTA）来定位最佳的穿孔器。不幸的是,这些技术既昂贵又耗时,而且在手术过程中无法执行.动态红外热成像(DIRT)可以为这些缺点提供解决方案。
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。
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。
