Clustering Propagation for Universal Medical Image Segmentation
Yuhang Ding Liulei Li Wenguan Wang Yi Yang
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.