Prominent solutions for medical image segmentation are typically tailored for
automatic or interactive setups, posing challenges in facilitating progress
achieved in one task to another.$_{\!}$ This$_{\!}$ also$_{\!}$
necessitates$_{\!}$ separate$_{\!}$ models for each task, duplicating both
training time and parameters.$_{\!}$ To$_{\!}$ address$_{\!}$ above$_{\!}$
issues,$_{\!}$ we$_{\!}$ introduce$_{\!}$ S2VNet,$_{\!}$ a$_{\!}$
universal$_{\!}$ framework$_{\!}$ that$_{\!}$ leverages$_{\!}$
Slice-to-Volume$_{\!}$ propagation$_{\!}$ to$_{\!}$ unify automatic/interactive
segmentation within a single model and one training session. Inspired by
clustering-based segmentation techniques, S2VNet makes full use of the
slice-wise structure of volumetric data by initializing cluster centers from
the cluster$_{\!}$ results$_{\!}$ of$_{\!}$ previous$_{\!}$ slice.$_{\!}$ This
enables knowledge acquired from prior slices to assist in the segmentation of
the current slice, further efficiently bridging the communication between
remote slices using mere 2D networks. Moreover, such a framework readily
accommodates interactive segmentation with no architectural change, simply by
initializing centroids from user inputs. S2VNet distinguishes itself by swift
inference speeds and reduced memory consumption compared to prevailing 3D
solutions. It can also handle multi-class interactions with each of them
serving to initialize different centroids. Experiments on three benchmarks
demonstrate S2VNet surpasses task-specified solutions on both
automatic/interactive setups.

医学图像分割的通用框架 S2VNet 利用切片到体积传播将自动 / 交互式分割统一在单一模型和一个训练会话中，通过从先前切片的聚类结果初始化聚类中心，使得框架可以高效地沟通远程切片，同时提供快速推断速度和较低的内存消耗。