Abstract
This study presents the development of a deep learning-based two-stage network designed for the efficient and precise segmentation of the femur in full lower limb CT images. The proposed network incorporates a dual-phase approach: rapid delineation of regions of interest followed by semantic segmentation of the femur. The experimental dataset comprises 100 samples obtained from a hospital, partitioned into 85 for training, 8 for validation, and 7 for testing. In the first stage, the model achieves an average Intersection over Union of 0.9671 and a mean Average Precision of 0.9656, effectively delineating the femoral region with high accuracy. During the second stage, the network attains an average Dice coefficient of 0.953, sensitivity of 0.965, specificity of 0.998, and pixel accuracy of 0.996, ensuring precise segmentation of the femur. When compared to the single-stage SegResNet architecture, the proposed two-stage model demonstrates faster convergence during training, reduced inference times, higher segmentation accuracy, and overall superior performance. Comparative evaluations against the TransUnet model further highlight the network's notable advantages in accuracy and robustness. In summary, the proposed two-stage network offers an efficient, accurate, and autonomous solution for femur segmentation in large-scale and complex medical imaging datasets. Requiring relatively modest training and computational resources, the model exhibits significant potential for scalability and clinical applicability, making it a valuable tool for advancing femoral image segmentation and supporting diagnostic workflows.