Surgical instrument segmentation is an essential task in the domain of computer-assisted surgical system. It is critical to increase the context-awareness of surgeons during the operation. We propose a new model based on the U-Net architecture for surgical instrument segmentation, which aggregates multi-scale feature maps and has cascaded dilated convolution layers. The model adopts dense upsampling convolution instead of deconvolution for upsampling. We set the side loss function on each side-output layer. The loss function includes an output loss function and all side loss functions to supervise the training of each layer. To validate our model, we compare our proposed model with advanced architecture U-Net in the dataset consisting of laparoscopy images from multiple surgical operations. Experiment results demonstrate that our model achieves good performance.

中文翻译：

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A Holistically-Nested U-Net：基于卷积神经网络的手术器械分割。

手术器械分割是计算机辅助手术系统领域的一项基本任务。在手术过程中提高外科医生的情境意识至关重要。我们提出了一种基于 U-Net 架构的用于手术器械分割的新模型，该模型聚合了多尺度特征图并具有级联扩张卷积层。该模型采用密集上采样卷积代替反卷积进行上采样。我们在每个侧输出层上设置侧损失函数。损失函数包括一个输出损失函数和所有边损失函数来监督每一层的训练。为了验证我们的模型，我们将我们提出的模型与来自多个外科手术的腹腔镜图像组成的数据集中的高级架构 U-Net 进行了比较。