Magnetic Resonance Imaging (MRI) plays a vital role in medical and biological applications. However, for patients with MRI-compatible implantable devices such as cochlear implants, the presence of integrated magnets often leads to large signal voids and severe artifacts, significantly compromising diagnostic accuracy. Although recent advances in deep learning have shown promise in artifact reduction and image enhancement, the quantitative assessment of artifact regions still heavily relies on manual annotation, which is labor-intensive and inconsistent. In particular, boundary distortions and tissue loss near cranial regions pose significant challenges for accurate artifact delineation, limiting the effectiveness of existing segmentation methods. To address these issues, we propose a novel 3D artifact segmentation framework that integrates reflective registration into a deep neural network combining U-Net and Transformer architectures. We conducted experiments on MRI data from 5 real-world patients with cochlear implants. Experimental results demonstrate that our method achieves state-of-the-art performance in implant-induced artifact segmentation, offering an efficient and reliable solution for automatic artifact evaluation in clinical settings.