PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement

Zhen huang, S Kevin Zhou

Abstract:

Purpose Pelvic X-ray (PXR) is widely utilized in clinical decision-making associated with the pelvis, the lower part of the
trunk that supports and balances the trunk. In particular, PXR-based landmark detection facilitates downstream analysis and
computer-assisted diagnosis and treatment of pelvic diseases. Although PXR has the advantages of low radiation and reduced
cost compared to computed tomography (CT), it characterizes the 2D pelvis-tissue superposition of 3D structures, which
may affect the accuracy of landmark detection in some cases. However, the superposition nature of PXR is implicitly handled
by existing deep learning-based landmark detection methods, which mainly design the deep network structures for better
detection performances. Explicit handling of the superposition nature of PXR is rarely done.
Methods In this paper, we explicitly focus on the superposition of X-ray images. Specifically, we propose a pelvis extraction
(PELE) module that consists of a decomposition network, a domain adaptation network, and an enhancement module, which
utilizes 3D prior anatomical knowledge in CT to guide and well isolate the pelvis from PXR, thereby eliminating the influence
of soft tissue for landmark detection. The extracted pelvis image, after enhancement, is then used for landmark detection.
Results We conduct an extensive evaluation based on two public 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.
Conclusion The design of PELE module can improve the accuracy of different pelvic landmark detection baselines, which
we believe is obviously conducive to the positioning and inspection of clinical landmarks and critical structures, thus better
serving downstream tasks. Our project has been open-sourced at https://github.com/ECNUACRush/PELEscores