This invention discloses an
artificial intelligence-based automatic four-zone segmentation and measurement
system for calcaneal fractures, relating to the field of three-dimensional
image analysis technology. The method acquires the
DICOM sequence of foot and
ankle CT scans, converts it into NIfTI three-dimensional volume data, and performs
voxel resampling, bone window normalization, and orientation
standardization. A first segmentation network extracts a complete three-dimensional
mask of the
calcaneus, establishes a three-dimensional anatomical coordinate
system, and clips the
region of interest (ROI) of the
calcaneus. A second segmentation network generates four-zone probability maps: the
anterior region, the
posterior region, the superior
articular surface-related region, and the medial support region. The spatial assignment of structural blocks is determined by constraints from the complete calcaneal three-dimensional
mask,
connected component analysis, and the
centroid and
anchor point projection of the structural blocks. Finally, based on the complete calcaneal contour and the four-zone
label map, geometric angles, surface height differences, the number of effective connected components, and the three-dimensional contact ratio are extracted and encapsulated into structured parameter files and
quality control markers for easy three-dimensional display and
image retrieval.