A clinical applicable study on lower limb segmentation from CT images for total knee arthroplasty

Segmentation of lower limb bones is essential for accurate preoperative planning in total knee arthroplasty (TKA), yet fully annotated CT datasets are rarely available in clinical practice. This study evaluated whether a partially supervised deep learning framework can leverage incompletely annotated CT data to generate anatomically accurate femur and tibia segmentations suitable for TKA planning.

A 3D nnU-Net model was trained using 205 healthy full-leg CT scans with mixed annotation completeness, including 17 fully annotated cases and partially labelled femur or tibia in the remaining scans. Performance was evaluated on an internal healthy dataset (n = 40), a cadaveric dataset (n = 15), and an osteoarthritis (OA) dataset acquired for robotic TKA planning (n = 10). Accuracy was assessed using Dice similarity coefficient (DSC), Hausdorff distance (HD), HD95, and root-mean-square surface distance (RMSE). Clinical relevance was evaluated using landmark localisation errors and joint alignment measurements (mLDFA and mPTA).

On the cadaveric dataset, mean DSC values were 96.53% (femur) and 97.41% (tibia), with RMSE < 1 mm. On the OA dataset, mean DSC remained approximately 96.5% with HD95 < 1.7 mm across acquisition windows. Alignment measurements derived from automatic and manual segmentations showed small differences (mLDFA: 86.81° vs 87.02°, p = 0.11; mPTA: 86.74° vs 87.29°, p = 0.08), with no statistically significant differences observed.

Partially supervised training enables accurate lower limb segmentation from incompletely annotated CT datasets and preserves clinically relevant alignment measurements required for TKA planning.