PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Michal Brzus; Kevin Knoernschild; Jessica C Sieren; Hans J Johnson

doi:10.20944/preprints202301.0313.v1

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PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

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PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Michal Brzus, Kevin Knoernschild, Jessica C Sieren and Hans J Johnson

01/17/2023

DOI: 10.20944/preprints202301.0313.v1

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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, 13 this is the first automated pipeline tool aimed at large animal images.

Brain

Minipig

Segmentation

Landmarks

Image Processing

Deep Learning

Pig

Details

Title: Subtitle: PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI
Creators: Michal Brzus (Author) - University of Iowa
Kevin Knoernschild - University of Iowa
Jessica C Sieren - University of Iowa, Radiology
Hans J Johnson (Author) - University of Iowa, The Iowa Institute for Biomedical Imaging
Resource Type: Preprint
DOI: 10.20944/preprints202301.0313.v1
Number of pages: 11 pages
Language: English
Date posted: 01/17/2023
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Stead Family Department of Pediatrics; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
Record Identifier: 9984364960302771

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