Multi-Scale Semantic Fusion of a Large Receptive Field for Irregular Pelvic X-Ray Landmark Detection

Pelvic landmark detection is a significant pre-task to measure the clinical measurement in pelvic abnormality analysis. Accurate pelvic landmark detection could provide reliable clinical parameter measurement results, which are helpful for doctors to diagnose and treat pelvic diseases. However, the multi-scale characteristics, temporal diversity, and pathological abnormalities of different pelvic X-rays bring enormous challenges to the landmark detection task. In order to retain strong robustness in irregular pelvic X-rays, we propose a novel, flexible two-stage framework. In the initial stage, a single neural network is employed to estimate the locations of every landmark simultaneously, enabling the identification of potential landmark regions. Then, the receptive field of candidate region proposals is expanded by 4 times through the receptive field amplification module. In the second stage, the landmark detection module fuses semantically rich features at different scales through a multi-scale semantic fusion module. So that the framework can fully learn the strongly relevant semantic information around the landmark at high resolution. We collected a data set of 430 pelvic X-rays, including a large number of irregular pelvic X-rays, to evaluate our framework. The experimental results demonstrate that our framework achieves a state-of-the-art detection mean radial error of 3.724 ± 4.247-mm. The experimental results show that the proposed method can help doctors quickly and accurately find the characteristic points of the pelvis and could be applied to clinical diagnosis.