Defect-adaptive landmark detection in pelvis CT images via personalized structure-aware learning.

Accurate localization of anatomical landmarks from pelvic CT images is crucial for preoperative planning in orthopedic procedures. However, existing automatic methods often underperform when facing defective bone structures, which are common in clinical scenarios involving trauma, resection, or severe degeneration. To address this challenge, we propose DADNet, a defect-adaptive detection network that incorporates personalized structural priors to achieve accurate and robust landmark detection in defective pelvis CT images. DADNet first constructs a structure-aware soft prior map that encodes the spatial distribution of landmarks based on the individual bone anatomy. This prior map, which highlights landmark-related regions, is generated via a dedicated convolutional module followed by logarithmic transformation. Guided by this soft prior, we extract local patches around the candidate regions and performs landmark regression using a patch-based context-aware detection network. To further enhance detection robustness in defective regions, we introduce a bone-aware detection loss that modulates the prediction confidence based on bone structures. The modulation weight is dynamically adjusted during training via a sigmoid scheduler, enabling progressive adaptation from coarse to fine structural constraints. We evaluate DADNet on both public and private datasets featuring varying degrees of pelvic defects. Our approach achieves an average detection error of 1.252 ± 0.075 mm on severely defective cases, significantly outperforming existing methods. The proposed framework demonstrates strong adaptability to anatomical variability and structural incompleteness, offering a promising tool for accurate and robust landmark detection in challenging clinical cases.