Multi-agent reinforcement learning pipeline for anatomical landmark detection in minipigs

Brzus, Michal; Powers, Alexander; Knoernschild, Kevin S.; Sieren, Jessica C.; Johnson, Hans J.

doi:10.1117/12.2611008

Cited by 1 publication

(3 citation statements)

References 17 publications

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“…The GWC masks were generated using Atropos software [14] and are used in training the GWC model. The physical land- Detailed information on data and implementation of landmark detection can be found in our previous work [11].…”

Section: Datamentioning

confidence: 99%

“…Detailed information about the models' architecture, training, and results can be found in our previous work [11].…”

Section: Landmarks Detectionmentioning

confidence: 99%

“…In this work, we propose PigSNIPE -a pipeline for the automated processing of minipig MR images, similar to software available for humans [10]. This is an extension of our previous work [11] 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.…”

Section: Introductionmentioning

confidence: 99%

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PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Brzus¹,

Knoernschild²,

Sieren³

et al. 2023

Preprint

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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.

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Section: Datamentioning

confidence: 99%

“…Detailed information about the models' architecture, training, and results can be found in our previous work [11].…”

Section: Landmarks Detectionmentioning

confidence: 99%