Thresholdmann:用于交互式创建适应性阈值以分割磁共振成像数据的网络工具。

K. Heuer, N. Traut, Roberto Toro
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引用次数: 0

摘要

大脑提取和分割是大多数神经成像分析的第一步。自动方法在成人大脑中效果良好,但在非人类数据中,由于肌肉组织、头骨和亮度梯度的影响,产生的结果并不可靠。Thresholdmann (https://neuroanatomy. github.io/thresholdmann)是一款开源网络工具,用于将空间变化阈值交互式应用于Nifti卷。无需下载或安装,所有处理都在用户电脑上完成。将 Nifti 体积拖放到网络应用程序上,即可在立体观察器中进行可视化探索。然后通过设置控制点来创建空间变化阈值,每个控制点都有自己的局部阈值。每个点都可以重新定位或移除,每个局部阈值都可以使用滑块或输入数值进行实时调整。阈值方向可以切换,以便在不同的成像模式(如 T1 和 T2 加权对比)下对感兴趣的结构进行分割。掩膜的不透明度以及核磁共振成像的亮度和对比度可通过滑块进行调整。可以计算阈值化掩膜的三维模型,在交互式三维渲染中检查结果。最后,可以保存阈值化掩膜、空间变化阈值和控制点列表,以便以后在脚本工作流程中使用,从原始数据中再现阈值化体积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thresholdmann: A Web tool for interactively creating adaptive thresholds to segment MRI data.
Brain extraction and segmentation are the first step for most neuroimaging analyses. Automatic methods work well in adult human brains, but produce unreliable results in non-human data, due to muscle tissue, skull, and luminosity gradients. Thresholdmann (https://neuroanatomy. github.io/thresholdmann) is an open source Web tool for the interactive application of space-varying thresholds to Nifti volumes. No download or installation are required and all processing is done on the user’s computer. Nifti volumes are dragged and dropped onto the Web app and become available for visual exploration in a stereotaxic viewer. A space-varying threshold is then created by setting control points, each with their own local threshold. Each point can be repositioned or removed, and each local threshold can be adjusted in real time using sliders or entering their values numerically. The threshold direction can be switched to allow segmentation of the structure of interest in different imaging modalities, such as T1 and T2 weighted contrasts. The opacity of the mask and the brightness and contrast of the MRI image can be adjusted via sliders. A 3D model of the thresholded mask can be computed to inspect the result in an interactive 3D render. Finally, the thresholded mask, the space varying threshold and the list of control points can be saved for later use in scripted workflows, able to reproduce the thresholded volume from the original data.
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