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.

通过包含与相邻区域的交互动态，重新构思边界生成机制，并提出了一种称为 PnPNet 的统一网络，用于建模混淆边界区域的形状特征。推拉分支提供了两种对抗性力量，以促使模型输出更精确的边界描绘。实验结果表明 PnPNet 在边界分割方面优于其他模型，并且推拉分支可以作为插拔式模块来增强经典的 U-shape 基准模型。

PnPNet：Pull-and-Push 网络用于有边界混淆的体积分割

PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary  Confusion

We tackle the problem of joint perception and motion forecasting in the
context of self-driving vehicles. Towards this goal we propose PnPNet, an
end-to-end model that takes as input sequential sensor data, and outputs at
each time step object tracks and their future trajectories. The key component
is a novel tracking module that generates object tracks online from detections
and exploits trajectory level features for motion forecasting. Specifically,
the object tracks get updated at each time step by solving both the data
association problem and the trajectory estimation problem. Importantly, the
whole model is end-to-end trainable and benefits from joint optimization of all
tasks. We validate PnPNet on two large-scale driving datasets, and show
significant improvements over the state-of-the-art with better occlusion
recovery and more accurate future prediction.

我们提出了 PnPNet，这是一种针对自动驾驶车辆中的联合感知和运动预测问题的端到端模型，通过在线生成物体轨迹并利用轨迹级特征进行运动预测，PnPNet 在两个大规模驾驶数据集上得到了验证，并显示了比现有技术更好的遮挡恢复和更准确的未来预测。