Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-Angle Feature Aggregation (MAFA) method, leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes. Secondly, in the end-to-end training stage, the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness, so that the contour shape of segmentation mask is more precise. The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons’ operations, and is compared to the existing methods on a public dataset collected with a da Vinci Xi Robot.

中文翻译：

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在内窥镜视觉中实现更好的手术器械分割：多角度特征聚合和轮廓监督

准确实时的手术器械分割在机器人辅助手术的内窥镜视觉中很重要，器械与组织的频繁接触和观察视角的不断变化带来了重大挑战。近年来，针对这些具有挑战性的任务，设计了越来越多的深度神经网络 (DNN) 模型。我们有动力提出一种通用的可嵌入方法来改进这些当前的 DNN 分割模型，而无需增加模型参数数量。首先，观察 DNN 有限的旋转不变性性能，我们提出了多角度特征聚合 (MAFA) 方法，利用主动图像旋转来获得更丰富的视觉线索，并使预测对仪器方向变化更具鲁棒性。其次，在端到端的训练阶段，利用辅助轮廓监督来引导模型学习边界意识，从而使分割mask的轮廓形状更加精确。所提出的方法通过对从外科医生手术中收集的新型 Sinus-Surgery 数据集的消融实验进行了验证，并与使用 da Vinci Xi 机器人收集的公共数据集上的现有方法进行了比较。