Two-Stage Method for Automatic Detection of Pelvic Landmarks

The fundamental premise of numerous clinical applications is automatic landmark detection of the pelvis and quantitative analysis of deformity. As a result, automatic pelvic landmark identification and quantitative analysis can give doctors with more intuitive and effective information to guide the creation of treatment programs and prognoses, which is extremely important. Previous research either employed less noisy infant pelvis datasets or the tested response time and detection accuracy that were insufficient for clinical usage. To address these issues, we propose a two-stage method for automatic detection of pelvic landmarks. The first stage uses heat map regression, the second stage uses coordinate regression, and both stages incorporate attention mechanisms. The framework comprehensively studies our data. The detection performance of the framework is evaluated: there is a 3.724mm MRE accuracy with an accuracy of 74.176, 78.117, 80.588, and 84.706% in the ranges of 4, 4.5, 5, and 6mm. As a result, the technology is expected to become an outstanding pelvic landmark detection tool, allowing doctors with minimum experience to quickly and accurately identify landmark.