Brzus, M.; Knoernschild, K.; Sieren, J.C.; Johnson, H.J. PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI. Algorithms2023, 16, 116.
Brzus, M.; Knoernschild, K.; Sieren, J.C.; Johnson, H.J. PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI. Algorithms 2023, 16, 116.
Brzus, M.; Knoernschild, K.; Sieren, J.C.; Johnson, H.J. PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI. Algorithms2023, 16, 116.
Brzus, M.; Knoernschild, K.; Sieren, J.C.; Johnson, H.J. PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI. Algorithms 2023, 16, 116.
Abstract
Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both brain similarity and economic advantages. Medical image processing is a crucial part of research as it allows researchers to monitor their experiments and understand disease development. However, although many algorithms are created and optimized for MR analysis of human data, those tools are not directly applicable or sufficiently sensitive to measure minipig data. In this work, we propose PigSNIPE - a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. The pipeline allows for image registration, AC-PC alignment, landmark detection, skull stripping, brainmasks and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images.
Keywords
Minipig; Brain; Segmentation; Landmarks; Image Processing; Deep Learning; Pig
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
