State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.
School of Medicine, The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University, Shanghai, China.
Med Biol Eng Comput. 2022 Jun;60(6):1613-1626. doi: 10.1007/s11517-022-02562-8. Epub 2022 Apr 9.
Gas embolism is a potentially serious complication of hysteroscopic surgery. It is particularly necessary to monitor bubble parameters in hysteroscopic images by computer vision method for helping develop automatic bubble removal devices. In this work, a framework combining a deep edge-aware network and marker-controlled watershed algorithm is presented to extract bubble parameters from hysteroscopy images. The proposed edge-aware network consists of an encoder-decoder architecture for bubble segmentation and a contour branch which is supervised by edge losses. The post-processing method based on marker-controlled watershed algorithm is used to further separate bubble instances and calculate size distribution. Extensive experiments substantiate that the proposed model achieves better performance than some typical segmentation methods. Accuracy, sensitivity, precision, Dice score, and mean intersection over union (mean IoU) obtained for the proposed edge-aware network are observed as 0.859 ± 0.017, 0.868 ± 0.019, 0.955 ± 0.005, 0.862 ± 0.005, and 0.758 ± 0.007, respectively. This work provides a valuable reference for automatic bubble removal devices in hysteroscopic surgery.
气栓是宫腔镜手术中一种潜在的严重并发症。通过计算机视觉方法监测宫腔镜图像中的气泡参数对于开发自动气泡去除设备尤为重要。在这项工作中，提出了一种结合深度边缘感知网络和标记控制分水岭算法的框架，用于从宫腔镜图像中提取气泡参数。所提出的边缘感知网络由用于气泡分割的编码器-解码器架构和由边缘损失监督的轮廓分支组成。基于标记控制分水岭算法的后处理方法用于进一步分离气泡实例并计算尺寸分布。广泛的实验证实，所提出的模型比一些典型的分割方法具有更好的性能。所提出的边缘感知网络的准确性、灵敏度、精度、Dice 得分和平均交并率（mean IoU）分别为 0.859±0.017、0.868±0.019、0.955±0.005、0.862±0.005 和 0.758±0.007。这项工作为宫腔镜手术中的自动气泡去除设备提供了有价值的参考。
