PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Michal Brzus; Kevin Knoernschild; Jessica Sieren; Hans Johnson

doi:10.3390/a16020116

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PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Journal article

Open access

Peer reviewed

PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Michal Brzus, Kevin Knoernschild, Jessica Sieren and Hans Johnson

Algorithms, Vol.16(2), 116

02/01/2023

DOI: 10.3390/a16020116

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https://doi.org/10.3390/a16020116View

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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 their 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. By pairing four reinforcement learning models and five deep learning UNet segmentation models with existing algorithms, we developed PigSNIPE, a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. PigSNIPE allows for image registration, AC-PC alignment, detection of 19 anatomical landmarks, skull stripping, brainmask 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, which can significantly reduce the time and resources needed for analyzing minipig neuroimages.

Algorithms

Automation

Brain

Machine Learning

Medical Imaging

Software

Animal health

Animals

Brain research

Cerebrospinal fluid

Datasets

Deep learning

Image processing

Image registration

Image segmentation

Medical research

Neurological diseases

Neurosciences

Registration

Details

Title: Subtitle: PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI
Creators: Michal Brzus - University of Iowa
Kevin Knoernschild - University of Iowa
Jessica Sieren - University of Iowa
Hans Johnson - University of Iowa
Resource Type: Journal article
Publication Details: Algorithms, Vol.16(2), 116
DOI: 10.3390/a16020116
eISSN: 1999-4893
Publisher: MDPI AG
Language: English
Date published: 02/01/2023
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: 9984368421002771

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