Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods

Abstract

Background

Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings.

Methods

Here, we present a new HTO landmark detection framework using an integration of optimised heatmap-offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model.

Results

Average Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip-knee-ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5.

Conclusion

The study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre-operative planning.