# PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI
## 1 Introduction  
Neurological disorders are common and predicted to affect one in six people [1]. Scientists constantly work to find more effective methods of diagnosing and treating these devastating conditions. Animal subjects are invaluable in neuroscience, as they allow researchers to induce diseases and their pathogenesis [2]. Small animals, most commonly mice, led to the development of many important animal models [3]. However, small animals lack the brain complexity and biological scale found in humans, resulting in many therapies failing to translate to human trials [4; 5]. This issue led to an increased focus on developing large animal models. Animals such as sheep, pigs, and non-human primates resulted in more robust findings [6]. Recently, minipigs have emerged at the forefront of neuroscience, as they present multiple advantages over other large animals, such as a relatively large gyrencephalic brain that is similar to the human brain's anatomy, neurophysiological processes, and white-to-gray matter ratio (60:40) [7]. Furthermore, the minipig's use presents a considerably lower cost and fewer ethical issues [8] when compared with its alternatives.  
Medical imaging, such as magnetic resonance (MR) imaging, provides researchers with non-invasive access to the brain. MRI produces higher-quality images compared with computed tomography without radiation risks, making it suitable for research. Medical image processing is a crucial part of research, as it allows researchers to monitor their experiments and understand disease development. Processes such as image registration, skull stripping, tissue segmentation, and landmark detection are necessary for many experiments.  
In recent years, deep learning has overtaken the field by outperforming virtually all previous algorithms and allowing for solving entirely new problems. Nowadays, most new AI developments, such as general adversarial networks and transformers, also succeed in medical imaging [9; 10]. However, many algorithms are created and optimized for MR analysis of human data and are not directly applicable or sufficiently sensitive to measure large-animal data. For example, due to differences in the size and shape of the head, highly successful tools such as SynthStrip [11] fail on minipig images. This forces researchers into laborious, expensive, and error-prone manual processing. Therefore, there is an urgent need for accurate and automated tools to analyze minipig data.  
In this work, we propose PigSNIPE, a pipeline for the automated processing of minipig MR images similar to software available for humans [12]. This is an extension of our previous work [13] and allows for image registration, AC-PC alignment, brain mask segmentation, skull stripping, tissue segmentation, caudate-putamen brain segmentation, and landmark detection in under two minutes. To the best of our knowledge, this is the first tool aimed at animal images which will dramatically reduce the time and resources needed to analyze minipig data and accelerate the scientific discovery process. Our tool is open source and can be found at [[https://github.com/BRAINSia/PigSNIPE](https://github.com/BRAINSia/PigSNIPE)]([https://github.com/BRAINSia/PigSNIPE](https://github.com/BRAINSia/PigSNIPE)) (accessed on 6 February 2023).