Accurate bronchoscope localization is essential for pulmonary interventions,
by providing six degrees of freedom (DOF) in airway navigation. However, the
robustness of current vision-based methods is often compromised in clinical
practice, and they struggle to perform in real-time and to generalize across
cases unseen during training. To overcome these challenges, we propose a novel
Probabilistic Airway Navigation System (PANS), leveraging Monte-Carlo method
with pose hypotheses and likelihoods to achieve robust and real-time
bronchoscope localization. Specifically, our PANS incorporates diverse visual
representations (\textit{e.g.}, odometry and landmarks) by leveraging two key
modules, including the Depth-based Motion Inference (DMI) and the Bronchial
Semantic Analysis (BSA). To generate the pose hypotheses of bronchoscope for
PANS, we devise the DMI to accurately propagate the estimation of pose
hypotheses over time. Moreover, to estimate the accurate pose likelihood, we
devise the BSA module by effectively distinguishing between similar bronchial
regions in endoscopic images, along with a novel metric to assess the
congruence between estimated depth maps and the segmented airway structure.
Under this probabilistic formulation, our PANS is capable of achieving the
6-DOF bronchoscope localization with superior accuracy and robustness.
Extensive experiments on the collected pulmonary intervention dataset
comprising 10 clinical cases confirm the advantage of our PANS over
state-of-the-arts, in terms of both robustness and generalization in localizing
deeper airway branches and the efficiency of real-time inference. The proposed
PANS reveals its potential to be a reliable tool in the operating room,
promising to enhance the quality and safety of pulmonary interventions.

利用概率导航系统 (PANS) 中的蒙特卡洛方法以及姿态假设和可能性，通过深度运动推断 (DMI) 和支气管语义分析 (BSA) 模块，实现了在肺部干预中具有优越的准确性和鲁棒性的 6 自由度支气管镜定位。

PANS：用于实时强健支气管镜定位的概率性空气道导航系统

PANS: Probabilistic Airway Navigation System for Real-time Robust  Bronchoscope Localization

In this paper, we explore degrees of freedom in deep sigmoidal neural
networks. We show that the degrees of freedom in these models is related to the
expected optimism, which is the expected difference between test error and
training error. We provide an efficient Monte-Carlo method to estimate the
degrees of freedom for multi-class classification methods. We show degrees of
freedom are lower than the parameter count in a simple XOR network. We extend
these results to neural nets trained on synthetic and real data, and
investigate impact of network's architecture and different regularization
choices. The degrees of freedom in deep networks are dramatically smaller than
the number of parameters, in some real datasets several orders of magnitude.
Further, we observe that for fixed number of parameters, deeper networks have
less degrees of freedom exhibiting a regularization-by-depth.

本研究探索了深度 S 形神经网络中的可自由度。我们发现可自由度与期望乐观主义 (即测试误差和训练误差之间的期望差异) 有关。我们提供了一种高效的 Monte-Carlo 方法来估计多类分类方法的可自由度。我们发现在一些实际数据集上，深度网络的自由度比参数数量小几个数量级，而对于固定数量的参数，更深层的网络具有更少的自由度，呈现出深度正则化的特点。