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对动态全身3D物图像的自动细分和预处理方法的比较,使用不同的物追踪器

Maria K Jaakkola¹, Marcela Xiomara Rivera Pineda², Rafael Díaz²

¹Turku PET Centre, University of Turku, Åbo Akademi University, and Turku University Hospital, Turku, Finland. maria.jaakkola@utu.fi.

Journal of imaging informatics in medicine

|May 27, 2025

概括

大多数无监督的细分方法都在与大型,现代的全身PET图像作斗争. 高斯混合模型和k-means集群显示出 PET 图像分析未来发展的前景.

科学领域:

医学成像医学成像
图像分析图像分析
计算科学是一种计算科学.

背景情况:

自动细分对于PET图像分析至关重要,但对于现代大型数据集而言,几乎没有工具.
现有的自动化方法往往过时,为较小的图像而设计,并且缺乏可用的实现.

研究的目的:

在大型,现代的全身PET图像上评估常用的无监督细分方法的可行性.
识别有前途的算法,用于未来的发展,在自动化PET图像细分.

主要方法:

在5个数据集的动态全身PET图像上测试了17个无监督细分算法.
包括各种预处理技术和细分方法.
通过使用Jaccard指数,子得分,精度和回忆来验证与手动细分的结果.

主要成果:

在17种方法中,只有6种方法对大型PET图像具有计算可行性.
层次聚类和HDBSCAN表现最低.
高斯混合模型 (GMM) 和k-means集群实现了0.58的Jaccard指数中位数,GMM在人类数据上表现优于k-means.

结论:

大多数无监督细分方法对于现代PET成像是计算不可行的.
高斯混合模型和k-means是进一步开发的最有前途的候选者.
预处理,特别是denoising,改善了结果,但细分小器官仍然具有挑战性.

关键词:

进行比较,比较.在这里,PET是PET.分段化分段化分段化分段化

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相关概念视频

Positron Emission Tomography

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...