PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Author:

Brzus Michal1ORCID,Knoernschild Kevin23ORCID,Sieren Jessica C.23ORCID,Johnson Hans J.12ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA

2. Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA

3. Department of Radiology, University of Iowa, Iowa City, IA 52246, USA

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.

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Reference37 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated registration-based skull stripping procedure for feline neuroimaging;NeuroImage;2024-10

2. DICOM sequence selection for medical imaging applications;Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications;2024-04-02

3. Leveraging High-Quality Research Data for Ischemic Stroke Lesion Segmentation on Clinical Data;2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI);2023-04-18

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