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Updated: Jul 2, 2025

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

在高分辨率和低分辨率CT成像之间,基于深度学习的轨迹骨微观结构的协调.

Indranil Guha¹, Syed Ahmed Nadeem², Xiaoliu Zhang¹

¹Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa, USA.

Medical physics

|February 28, 2024

概括

一种新的深度学习方法协调了低分辨率和高分辨率CT扫描,用于骨质疏松症研究. 3DGAN-CIRCLE模型改进了骨微结构分析,提高了多地点研究的准确性.

关键词:

在3DGAN-CIRCLE中使用.在CT成像中使用CT成像.深度学习是一种深度学习.统一化和化统一化和化高分辨率重建的重建.微观结构的微观结构骨质疏松症是一种骨质疏松症.脊椎骨的骨架骨的骨架骨.

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科学领域:

医疗成像医学成像
生物医学工程生物医学工程
骨质疏松症研究骨质疏松症研究

背景情况:

骨质疏松症的诊断依赖于骨矿物质密度和微观结构.
临床CT能够在体内进行骨微结构成像.
在CT扫描仪分辨率的变化需要图像协调一致的指标.

研究的目的:

开发和评估一种深度学习 (DL) 方法,以协调来自低分辨率和高分辨率CT扫描仪的骨微结构图像.
评估该方法在图像数据和衍生微观结构指标上的性能.

主要方法:

开发了GAN-CIRCLE的3D版本,利用两个生成对抗网络 (GAN),用于CT图像分辨率的协调.
该模型学会将低分辨率CT (LRCT) 映射到高分辨率CT (HRCT),反之亦然.
在20名志愿者的LRCT和HRCT图像块上进行了监督和无监督的培训/评估.

主要成果:

与LRCT相比,监督和无监督的3DGAN-CIRCLE方法显著改善了结构相似性 (SSIM).
与LRCT和无监督方法相比,监督的3DGAN-CIRCLE显示出对轨道 (Tb) 测量有更高的一致性 (CCC).
监督方法减少了Tb测量的偏差和变异性,优于现有的DL方法.

结论:

3DGAN-CIRCLE有效地生成具有与真实HRCT高度结构相似性的HRCT图像.
监督的3DGAN-CIRCLE提高了微观结构测量的准确性,并且优于无监督方法.
这种DL解决方案有助于协调用于纵向骨质疏松症研究的多部位成像数据.