Surgical instrument segmentation is extremely important for computer-assisted surgery. Different from common object segmentation, it is more challenging due to the large illumination and scale variation caused by the special surgical scenes. In this paper, we propose a novel bilinear attention network with adaptive receptive field to solve these two challenges. For the illumination variation, the bilinear attention module can capture second-order statistics to encode global contexts and semantic dependencies between local pixels. With them, semantic features in challenging areas can be inferred from their neighbors and the distinction of various semantics can be boosted. For the scale variation, our adaptive receptive field module aggregates multi-scale features and automatically fuses them with different weights. Specifically, it encodes the semantic relationship between channels to emphasize feature maps with appropriate scales, changing the receptive field of subsequent convolutions. The proposed network achieves the best performance 97.47% mean IOU on Cata7 and comes first place on EndoVis 2017 by 10.10% IOU overtaking second-ranking method.

中文翻译：

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BARNet：用于手术器械分割的具有自适应感受野的双线性注意网络

手术器械分割对于计算机辅助手术极为重要。与普通物体分割不同，由于特殊的手术场景引起的光照和尺度变化较大，因此更具挑战性。在本文中，我们提出了一种具有自适应感受野的新型双线性注意网络来解决这两个挑战。对于光照变化，双线性注意模块可以捕获二阶统计数据来编码全局上下文和局部像素之间的语义依赖关系。有了它们，可以从邻居中推断出具有挑战性的区域的语义特征，并可以增强各种语义的区别。对于尺度变化，我们的自适应感受野模块聚合多尺度特征并自动将它们与不同的权重融合。具体来说，它对通道之间的语义关系进行编码，以强调具有适当尺度的特征图，从而改变后续卷积的感受野。所提出的网络在 Cata7 上实现了 97.47% 的平均 IOU 的最佳性能，并在 EndoVis 2017 上以 10.10% 的 IOU 超过排名第二的方法排名第一。