In recent years, deep convolutional neural networks (CNNs) has made great achievements in the field of medical image segmentation, among which residual structure plays a significant role in the rapid development of CNN-based segmentation. However, the 3D residual networks inevitably bring a huge computational burden to machines for network inference, thus limiting their usages for many real clinical applications. To tackle this issue, we propose AutoPath, an image-specific inference approach for more efficient 3D segmentations. The proposed AutoPath dynamically selects enabled residual blocks regarding different input images during inference, thus effectively reducing total computation without degrading segmentation performance. To achieve this, a policy network is trained using reinforcement learning, by employing the rewards of using a minimal set of residual blocks and meanwhile maintaining accurate segmentation. Experimental results on liver CT dataset show that our approach not only provides efficient inference procedure but also attains satisfactory segmentation performance.

中文翻译：

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AutoPath：用于3D分割的特定于图像的推理。

近年来，深度卷积神经网络（CNN）在医学图像分割领域取得了巨大成就，其中残差结构在基于CNN的分割的快速发展中起着重要作用。然而，3D残差网络不可避免地给机器进行网络推理带来了巨大的计算负担，从而限制了它们在许多实际临床应用中的使用。为解决此问题，我们提出了AutoPath，这是一种针对特定图像的推理方法，可实现更高效的3D分割。所提出的AutoPath在推理过程中针对不同的输入图像动态选择启用的残差块，从而有效地减少了总计算量而不会降低分割性能。为此，我们会通过强化学习来训练政策网络，通过利用使用最少的残留块集并同时保持精确分割的奖励。在肝脏CT数据集上的实验结果表明，我们的方法不仅提供了有效的推理程序，而且还获得了令人满意的分割性能。