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.

翻译：精确的体素图像边界分割是图像引导诊断和计算机辅助干预中的关键任务，尤其针对临床实践中常见的边界混淆问题。然而，U形网络因缺乏边界形状约束而无法有效解决这一挑战。此外，现有边界细化方法过度强调细长结构，导致网络因建模微小对象能力受限而产生过拟合现象。本文从相邻区域交互动态的角度重新概念化边界生成机制，并提出名为PnPNet的统一网络以建模混淆边界区域的形状特征。PnPNet的核心组件包含推分支和拉分支。具体而言，基于扩散理论，我们从推分支设计了语义差异模块（SDM）来压缩边界区域。SDM内部的显式与隐式差分信息显著提升了类间边界的表征能力。此外，受K均值算法启发，引入拉分支中的类聚类模块（CCM）以拉伸交叉边界区域。由此，推拉分支分别收缩和扩大边界不确定性区域，两种对抗力共同促进模型输出更精确的边界划分。我们在三个具有挑战性的公开数据集和一个内部数据集上开展实验，涵盖模型预测中三种类型的边界混淆。实验结果表明PnPNet在HD和ASSD评估指标上显著优于其他分割网络。同时，推拉分支可作为即插即用模块增强经典U形基线模型。代码已开源。