The pelvis, the lower part of the trunk, supports and balances the trunk.
Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis
and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs
have the advantages of low radiation and reduced cost compared to computed
tomography (CT) images, their 2D pelvis-tissue superposition of 3D structures
confuses clinical decision-making. In this paper, we propose a PELvis
Extraction (PELE) module that utilizes 3D prior anatomical knowledge in CT to
guide and well isolate the pelvis from PXRs, thereby eliminating the influence
of soft tissue. We conduct an extensive evaluation based on two public datasets
and one private dataset, totaling 850 PXRs. The experimental results show that
the proposed PELE module significantly improves the accuracy of PXRs landmark
detection and achieves state-of-the-art performances in several benchmark
metrics, thus better serving downstream tasks.

该研究提出 PELvis Extraction（PELE）模块，利用 CT 中的 3D 解剖知识引导和很好地分离 PXRs 中的盆骨，从而消除软组织的影响，从而显著提高 PXRs 的地标检测准确性，并在几个基准度量上实现了最先进的性能。

PELE scores: 骨盆提取增强法用于骨盆 X 线特征点检测

PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and  Enhancement

Multi-region segmentation algorithms often have the onus of incorporating
complex anatomical knowledge representing spatial or geometric relationships
between objects, and general-purpose methods of addressing this knowledge in an
optimization-based manner have thus been lacking. This paper presents
Generalized Hierarchical Max-Flow (GHMF) segmentation, which captures simple
anatomical part-whole relationships in the form of an unconstrained hierarchy.
Regularization can then be applied to both parts and wholes independently,
allowing for spatial grouping and clustering of labels in a globally optimal
convex optimization framework. For the purposes of ready integration into a
variety of segmentation tasks, the hierarchies can be presented in run-time,
allowing for the segmentation problem to be readily specified and alternatives
explored without undue programming effort or recompilation.

该论文提出了一种广义分层最大流 (GHMF) 分割算法，该算法可以在全局最优凸优化框架中将解剖学的部分 - 整体关系表现为非约束性分层，从而实现标签的空间分组和聚类，并适应于多种分割任务中。