Fully Automated and Scalable Pipeline for Macaque Brain Registration
Authors: Junjie Zhuo*, Zhanbo Zhang*, Chi Xiao, Mingli Wang, Shukang Bi, Liang Shi, Qingming Luo, Hui Gong, Zhiming Shen**, Xiaoquan Yang**, and Pengcheng Li**

* These authors contributed equally.
*Correspondence to: zmshen@ion.ac.cn, xyang@hainanu.edu.cn, and pengchengli@hainanu.edu.cn

Overview
We develop Macaca-Star (Style transfer-based automated registration pipeline for Macaque), which incorporate deep learning model and self-individual MRI to address the cross-modal and ex vivo sample deformation challenges in macaque whole-brain registration. Macaca-Star is a fully automated pipeline that can register high-resolution macaque brain slices (~3 µm thickness in the z axis), such as fMOST, and classical section slice images (40~240 µm thickness in the z axis) to the NMT standard space.

fMOST PI and related self-individual MRI:

• PI (post-processed) 7MB.
• T1w MRI 27.5MB.

Classical section slice images and related self-individual MRI:

• Blockface image 59.5MB.
• Fluorescence image 17.9MB.
• T1w MRI 5.5MB.

Codes:
Detailed information of codes in this paper can be accessed at https://github.com/HNU-BIE/Macaca-Star