用于同时自动调整三维核素心脏图像方向和分割的多尺度空间变换器 U-Net

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-04-01 DOI:10.1109/TRPMS.2024.3382318
Yangfan Ni;Duo Zhang;Gege Ma;Fan Rao;Yuanfeng Wu;Lijun Lu;Zhongke Huang;Wentao Zhu
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引用次数: 0

摘要

左心室(LV)的准确重新定向和分割对于心肌灌注成像(MPI)的定量分析至关重要。本研究提出了一种名为多尺度空间变换器 UNet(MS-ST-UNet)的端到端模型,该模型包括多尺度空间变换器网络(MSSTN)和多尺度 UNet(MSUNet)模块,可同时对心脏核图像中的左心室区域进行重新定向和分割。多尺度采样器生成不同分辨率的图像,而尺度变换器(ST)模块则用于对齐特征的尺度。使用两种不同的心脏核图像模式对所提出的方法进行了训练和测试:$^{13}\text{N}$ -氨正电子发射断层扫描(PET)和$^{99m}$ Tc-sestamibi单光子发射计算机断层扫描(SPECT)。MS-ST-UNet 对 PET 左心室心肌(LV-MY)和 SPECT 左心室心肌的骰子相似系数(DSC)分别达到 91.48% 和 94.81%。此外,预测的刚性配准参数与地面实况之间的均方误差(MSE)减小到 1.4 (times 10^{-2}$ )以下。实验结果表明,与现有方法相比,MS-ST-UNet 能显著减少配准误差,并能更精确地检测 LV 结构的边界。这种联合学习框架促进了调整方向和分割任务之间的相互增强,从而实现了最先进的性能和高效的图像处理工作流程。
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A Multiscale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3-D Nuclear Cardiac Images
Accurate reorientation and segmentation of the left ventricular (LV) is essential for the quantitative analysis of myocardial perfusion imaging (MPI). This study proposes an end-to-end model, named as multiscale spatial transformer UNet (MS-ST-UNet), which involves the multiscale spatial transformer network (MSSTN) and multiscale UNet (MSUNet) modules to perform simultaneous reorientation and segmentation of LV region from nuclear cardiac images. The multiscale sampler produces images with varying resolutions, while scale transformer (ST) blocks are employed to align the scales of features. The proposed method is trained and tested using two different nuclear cardiac image modalities: $^{13}\text{N}$ -ammonia positron emission tomography (PET) and $^{99m}$ Tc-sestamibi single-photon emission computed tomography (SPECT). MS-ST-UNet attains dice similarity coefficient (DSC) scores of 91.48% and 94.81% for PET LV myocardium (LV-MY) and SPECT LV-MY, respectively. Additionally, the mean-square error (MSE) between predicted rigid registration parameters and ground truth decreases to below $1.4 \times 10^{-2}$ . The experimental findings indicate that the MS-ST-UNet yields notably reduced registration errors and more precise boundary detection for the LV structure compared to existing methods. This joint learning framework promotes mutual enhancement between reorientation and segmentation tasks, leading to cutting edge performance and an efficient image processing workflow.
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents Introducing IEEE Collabratec IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information Member Get-a-Member (MGM) Program
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