  Precise boundary segmentation of volumetric images is a critical task for
image-guided diagnosis and computer-assisted intervention, especially for
boundary confusion in clinical practice. However, U-shape networks cannot
effectively resolve this challenge due to the lack of boundary shape
constraints. Besides, existing methods of refining boundaries overemphasize the
slender structure, which results in the overfitting phenomenon due to networks'
limited abilities to model tiny objects. In this paper, we reconceptualize the
mechanism of boundary generation by encompassing the interaction dynamics with
adjacent regions. Moreover, we propose a unified network termed PnPNet to model
shape characteristics of the confused boundary region. Core ingredients of
PnPNet contain the pushing and pulling branches. Specifically, based on
diffusion theory, we devise the semantic difference module (SDM) from the
pushing branch to squeeze the boundary region. Explicit and implicit
differential information inside SDM significantly boost representation
abilities for inter-class boundaries. Additionally, motivated by the K-means
algorithm, the class clustering module (CCM) from the pulling branch is
introduced to stretch the intersected boundary region. Thus, pushing and
pulling branches will shrink and enlarge the boundary uncertainty respectively.
They furnish two adversarial forces to promote models to output a more precise
delineation of boundaries. We carry out experiments on three challenging public
datasets and one in-house dataset, containing three types of boundary confusion
in model predictions. Experimental results demonstrate the superiority of
PnPNet over other segmentation networks, especially on evaluation metrics of HD
and ASSD. Besides, pushing and pulling branches can serve as plug-and-play
modules to enhance classic U-shape baseline models. Codes are available.
