Abstract
Osteonecrosis of the Femoral Head (ONFH) is a progressive disease characterized by the death of bone cells due to the loss of blood supply. Early detection and treatment of this disease are vital in avoiding Total Hip Replacement. Early stages of ONFH can be diagnosed using Magnetic Resonance Imaging (MRI), commonly used intra-operative imaging modalities such as fluoroscopy frequently fail to depict the lesion. Therefore, increasing the difficulty of intra-operative localization of osteonecrosis. This work introduces a novel framework that enables the localization of necrotic lesions in Computed Tomography (CT) as a step toward localizing and visualizing necrotic lesions in intra-operative images. The proposed framework uses Deep Learning algorithms to enable automatic segmentation of femur, pelvis, and necrotic lesions in MRI. An additional step performs semi-automatic segmentation of these anatomies, excluding the necrotic lesions, in CT. A final step performs pairwise registration of the corresponding anatomies, allowing for the localization and visualization of the necrosis in CT. To investigate the feasibility of integrating the proposed framework in the surgical workflow, we conducted experiments on MRIs and CTs containing early-stage ONFH. Our results indicate that the proposed framework is able to segment the anatomical structures of interest and accurately register the femurs and pelvis of the corresponding volumes, allowing for the visualization and localization of the ONFH in CT and generated X-rays, which could enable intra-operative visualization of the necrotic lesions for surgical procedures such as core decompression of the femur.