A Hybrid Propagation Network for Interactive Volumetric Image Segmentation

Luyue Shi, Xuanye Zhang, Yunbi Liu, Xiaoguang Han

Abstract: Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical volumetric data. Given a 3D volumetric image, the user interaction can only be performed on a few slices, thus the key issue is how to propagate the information over the entire volume for spatial-consistent segmentation. In this paper, we propose a novel hybrid propagation network for interactive segmentation of 3D medical images. Our proposed method consists of two key designs, including a slice propagation network (denoted as SPN) for transferring user hints to adjacent slices to guide the segmentation slice-by-slice and a volume propagation network (denoted as VPN) for propagating user hints over the entire volume in a global manner. Specifically, as for SPN, we adopt a memory-augmented network, which utilizes the information of segmented slices (memory slices) to propagate interaction information. To use interaction information propagated by VPN, a feature-enhanced memory module is designed, in which the volume segmentation information from the latent space of VPN is introduced into the memory module of SPN. In such a way, the interactive segmentation can leverage both advantages of volume and slice propagation, thus improving the volume segmentation results. We perform experiments on two commonly-used 3D medical datasets, with the experimental results indicating that our method outperforms the state-of-the-art methods. Our code is available at https://github.com/luyueshi/Hybrid-Propagation .