Articular surface segmentation using active shape models for intraoperative implant assessment.

PURPOSE

In orthopedic surgeries, it is important to avoid intra-articular implant placements, which increase revision rates and the risk of arthritis. In order to support the intraoperative assessment and correction of surgical implants, we present an automatic detection approach using cone-beam computed tomography (CBCT).

METHODS

Multiple active shape models (ASM) with specified articular surface regions are used to isolate the joint spaces. Fast and easy-to-implement methods are integrated in the ASM segmentation to optimize the robustness and accuracy for intraoperative application. A cylinder detection method is applied to determine metal implants. Intersections between articular surfaces and cylinders are detected and used to find intra-articular collisions.

RESULTS

Segmentations of two calcaneal articular surfaces were evaluated on 50 patient images and have shown average surface distance errors of 0.59 and 0.46 mm, respectively. The proposed model-independent segmentation at the specified articular surface regions allowed to significantly decrease the error by 22 and 25 % on average. The method was able to compensate suboptimal initializations for translations of up to 16 mm and rotations of up to 21[Formula: see text]. In a human cadaver test, articular perforations could be localized with an accuracy of 0.80 mm on average.

CONCLUSIONS

A concept for automatic intraoperative detection of intra-articular implants in CBCT images was presented. The results show a reliable segmentation of articular surfaces in retrospective patient data and an accurate localization of misplaced implants in artificially created human cadaver test cases.