Osteoarthritis is a degenerative disease that affects the aging population by degrading the cartilage in the joints. The early and accurate diagnosis of this disease is key to effective treatment. For an early and accurate diagnosis of this disease, clinicians often use X-ray imaging. This allows medical professionals to manually measure the joint space width (JSW) in X-rays images to determine the progression of the disease. This method however proves to be both time-consuming and variable based on the professional. This research addresses the automation of the measurement of the JSW for the hip, using deep learning techniques, to improve precision and efficiency. The automated measurement of the JSW is challenged by variations in the imaging conditions across different clinical settings. To address these discrepancies and keep a good performance, domain adaptation techniques are used to counter these domain shifts to ensure a consistent JSW segmentation across different imaging domains. The study investigates whether a specific domain adaptation technique can enhance the accuracy and robustness of deep learning models specifically for femur segmentation in X-ray images across different datasets. A base deep learning model is developed for femur segmentation, and supervised domain adaptation is applied. The study compares the performance of the adapted model with the base model across two different datasets. Results indicate that supervised domain adaptation does not significantly improve the model’s robustness and accuracy in femur segmentation among two different datasets. These unexpected findings suggest that incorporating domain adaptation techniques may not always lead to a more reliable and efficient diagnosis of osteoarthritis, reducing the manual workload for clinicians.