This paper presents a reproducible evaluation of the performance of the ANTs (Advanced Neuroimaging Tools) similarity metric in brain image registration. ANTs is an open-source software package built on the Insight Toolkit (ITK) framework, providing tools for image registration, template building, and segmentation. The study evaluates the performance of ANTs' Symmetric Normalization (SyN) transformation model, which consistently performs well in various registration tasks. The paper discusses the ANTs transformation models, similarity metrics, and optimization strategies, and evaluates their performance on a large-scale dataset (LPBA40) for brain labeling and registration. The study compares the performance of different similarity metrics (MSQ, CC, MI) and transformation models (affine, diffeomorphic) in brain image registration. The results show that the MI metric performs best for affine registration, while the SyN transformation model provides the best overall performance in registration tasks. The study also evaluates the impact of different similarity metrics on template construction and brain labeling, and finds that the MI metric provides the best initialization for deformable registration. The paper highlights the importance of affine initialization in deformable registration and shows that the MI metric is robust to scanner variations and pathomorphological changes. The study also discusses the computational efficiency of different similarity metrics, finding that the MSQ metric is the fastest, while the CC metric is the most time-consuming. The results suggest that the MI metric is the best choice for whole head affine registration, and that the SyN transformation model provides the best overall performance in registration tasks. The study also discusses the impact of quality affine initialization on brain labeling performance, finding that the initial affine registration result is highly correlated with the final deformable registration result. The paper concludes that the ANTs software package provides a robust and efficient tool for brain image registration and that the SyN transformation model and MI similarity metric are the best choices for achieving accurate and reliable registration results.This paper presents a reproducible evaluation of the performance of the ANTs (Advanced Neuroimaging Tools) similarity metric in brain image registration. ANTs is an open-source software package built on the Insight Toolkit (ITK) framework, providing tools for image registration, template building, and segmentation. The study evaluates the performance of ANTs' Symmetric Normalization (SyN) transformation model, which consistently performs well in various registration tasks. The paper discusses the ANTs transformation models, similarity metrics, and optimization strategies, and evaluates their performance on a large-scale dataset (LPBA40) for brain labeling and registration. The study compares the performance of different similarity metrics (MSQ, CC, MI) and transformation models (affine, diffeomorphic) in brain image registration. The results show that the MI metric performs best for affine registration, while the SyN transformation model provides the best overall performance in registration tasks. The study also evaluates the impact of different similarity metrics on template construction and brain labeling, and finds that the MI metric provides the best initialization for deformable registration. The paper highlights the importance of affine initialization in deformable registration and shows that the MI metric is robust to scanner variations and pathomorphological changes. The study also discusses the computational efficiency of different similarity metrics, finding that the MSQ metric is the fastest, while the CC metric is the most time-consuming. The results suggest that the MI metric is the best choice for whole head affine registration, and that the SyN transformation model provides the best overall performance in registration tasks. The study also discusses the impact of quality affine initialization on brain labeling performance, finding that the initial affine registration result is highly correlated with the final deformable registration result. The paper concludes that the ANTs software package provides a robust and efficient tool for brain image registration and that the SyN transformation model and MI similarity metric are the best choices for achieving accurate and reliable registration results.