Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2024-09-09
DOI
https://doi.org/10.1002/aisy.202400007
Citations
6
Primary Topic
Characterization and Applications of Magnetic Nanoparticles
Type
article
Field-Weighted Citation Impact
1.11

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article

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Yifan Liu, Yu L. Lei, Harvey L. Bumpers, Xuefei Huang et al.
6 citations
Advanced Intelligent Systems
Characterization and Applications of Magnetic Nanoparticles
1.11
article

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Yifan Liu, Yu L. Lei, Harvey L. Bumpers, Xuefei Huang, Bo Li, Gary D. Luker, Cheng‐You Yao, Aniwat Juhong, Chia‐Wei Yang, Wibool Piyawattanametha, Dalen Agnew, Zhen Qiu
article en
6 citations

Abstract

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Advanced Intelligent SystemsVol. 6(10)
University of Michigan (US), Michigan State University (US), King Mongkut's Institute of Technology Ladkrabang (TH)
National Science Foundation, U.S. Department of Energy, National Research Council of Thailand, Ministry of Higher Education, Science, Research and Innovation, Thailand
Good health and well-being
Openalex Percentile: Top 24%
Characterization and Applications of Magnetic Nanoparticles
1.11
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