Abstract
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.
Highlights
Automated segmentation is useful to assist doctors in disease diagnosis and surgical/treatment planning
This paper explores the problem of dynamically distributing computation across all residual blocks in a trained ResNet for image-specific segmentation inference
This paper develops a reinforcement learning method to select image-specific and efficient inference paths for 3D segmentation, which addresses the problem of huge computational burden for 3D segmentation networks
Summary
Automated segmentation is useful to assist doctors in disease diagnosis and surgical/treatment planning. Various architectures of deep convolutional neural networks (CNNs) have been proposed and successfully introduced to many segmentation applications. The residual structures in ResNet (He et al, 2016) play an important role in the rapid development of CNN-based segmentation. The backbone which contains the residual blocks has become essential support for many segmentation models, such as DeepLab V3 (Chen et al, 2017), HD-Net (Jia et al, 2019), Res-UNet (Xiao et al, 2018), and so on. Despite the superior performance of residual blocks, these structures inevitably bring a huge computational burden for network inference. This leads to the difficulty of introducing deep models (such as 3D ResNet-50/101) in clinical practice
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