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.

翻译：针对医学图像分割的主流解决方案通常针对自动或交互式设置进行专门设计，这导致不同任务间的进展难以相互促进，并需要为每个任务单独训练模型，造成训练时间和参数的重复浪费。为解决上述问题，我们提出了S2VNet——一种利用切片到体积传播（Slice-to-Volume propagation）的通用框架，可在单个模型和单次训练中统一实现自动/交互式分割。受基于聚类的分割技术启发，S2VNet通过从前一片切片的聚类结果中初始化聚类中心，充分利用体数据切片级结构信息。这使得先验切片获取的知识能辅助当前切片的分割，进而通过仅用2D网络高效桥接远程切片间的通信。此外，该框架无需修改架构即可通过从用户输入初始化质心直接支持交互式分割，每个质心对应不同的类别交互。与主流3D方案相比，S2VNet以更快的推理速度和更少的内存消耗脱颖而出，且能处理多类别交互场景。在三个基准数据集上的实验表明，S2VNet在自动/交互式两种设置下均超越了任务专用解决方案。