  Knee osteoarthritis is a degenerative joint disease that induces chronic pain
and disability. Bone morphological analysis is a promising tool to understand
the mechanical aspect of this disorder. This study proposes a 2D bone
morphological analysis using manually segmented bones to explore morphological
features related to distinct pain conditions. Furthermore, six semantic
segmentation algorithms are assessed for extracting femur and tibia bones from
X-ray images. Our analysis reveals that the morphology of the femur undergoes
significant changes in instances where pain worsens. Conversely, improvements
in pain may not manifest pronounced alterations in bone shape. The
few-shot-learning-based algorithm, UniverSeg, demonstrated superior
segmentation results with Dice scores of 99.69% for femur and 99.60% for tibia.
Regarding pain condition classification, the zero-shot-learning-based
algorithm, CP-SAM, achieved the highest accuracy at 66% among all models.
UniverSeg is recommended for automatic knee bone segmentation, while SAM models
show potential with prompt encoder modifications for optimized outcomes. These
findings highlight the effectiveness of few-shot learning for semantic
segmentation and the potential of zero-shot learning in enhancing
classification models for knee osteoarthritis diagnosis.
