Robust multi-scale anatomical landmark detection in incomplete 3D-CT data

Abstract

An essential prerequisite for comprehensive medical image analysis is the robust and fast detection of anatomical structures in the human body. To this point, machine learning techniques are most often applied to address this problem, exploiting large annotated image databases to estimate parametric models for anatomy appearance. However, the performance of these methods is generally limited, due to suboptimal and exhaustive search strategies applied on large volumetric image data, e.g., 3D-CT scans.