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

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-09-09 DOI:10.1002/aisy.202400007
Aniwat Juhong, Bo Li, Yifan Liu, Chia-Wei Yang, Cheng-You Yao, Dalen W. Agnew, Yu Leo Lei, Gary D. Luker, Harvey Bumpers, Xuefei Huang, Wibool Piyawattanametha, Zhen Qiu
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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.

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用于磁粉成像-计算机断层扫描图像分割的多头注意力 U-Net
磁粉成像(MPI)是一种新兴的无创分子成像方式,具有高灵敏度和高特异性、卓越的线性定量能力以及成功应用于临床的潜力。计算机断层扫描(CT)通常与 MPI 图像相结合,以获得更多的解剖信息。本文介绍了一种基于深度学习的 MPI-CT 图像分割方法。训练所提出的深度学习模型所使用的数据集来自乳腺癌转基因小鼠模型,该模型在施用吲哚菁绿(ICG)共轭超顺磁性氧化铁纳米虫(NWs-ICG)作为示踪剂后获得。由于增强的渗透性和滞留(EPR)效应,NWs-ICG 颗粒会在肿瘤中逐渐累积。所提出的深度学习模型利用了多头注意机制和 U-Net 模型的优势,对 MPI-CT 图像进行分割,取得了极佳的效果。此外,该模型还采用了不同数量的注意力头,以探索我们定制的 MPI-CT 数据集的最佳数量。
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4 weeks
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