In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization framework. Moreover, we employ an integral operation to make the mapping from heatmaps to coordinates differentiable to further enhance heatmap-based localization with the learned distribution prior. Our proposed Normalizing Flow-based Distribution Prior (NFDP) employs a straightforward backbone and non-problem-tailored architecture (i.e., ResNet18), which delivers high-fidelity outputs across three X-ray-based landmark localization datasets. Remarkably, the proposed NFDP can do the job with minimal additional computational burden as the normalizing flows module is detached from the framework on inferencing. As compared to existing techniques, our proposed NFDP provides a superior balance between prediction accuracy and inference speed, making it a highly efficient and effective approach. The source code of this paper is available at https://github.com/jacksonhzx95/NFDP.

中文翻译：

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具有生成分布先验的医学图像的地标定位


在医学图像分析中，解剖标志通常包含对其结构信息的强大先验知识。在本文中，我们建议通过标准化流对底层地标分布进行建模来促进医学地标定位。具体来说，我们将基于流的地标分布先验作为可学习的目标函数引入到基于回归的地标定位框架中。此外，我们采用积分运算使从热图到坐标的映射可微分，以进一步增强基于学习分布先验的基于热图的定位。我们提出的基于流的归一化分布先验 (NFDP) 采用简单的主干和非问题定制的架构（即 ResNet18），它在三个基于 X 射线的地标定位数据集上提供高保真输出。值得注意的是，由于标准化流模块与推理框架分离，所提出的 NFDP 可以以最小的额外计算负担完成这项工作。与现有技术相比，我们提出的 NFDP 在预测精度和推理速度之间提供了卓越的平衡，使其成为一种高效且有效的方法。本文的源代码可在https://github.com/jacksonhzx95/NFDP获取。