User friendly, cloud based, whole slide image segmentation.
User friendly, cloud based, whole slide image segmentation.
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用户友好、基于云的整个幻灯片图像分割。
DOI：
10.1117/12.2581383
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sarder,Pinaki
中科院分区：
文献类型：
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作者：
Lutnick,Brendon;KammardiShashiprakash,Avinash;Manthey,David;Sarder,Pinaki
卷积神经网络是图像分割的最新技术，已经被许多计算研究人员成功地应用于组织学图像。然而，由于代码的复杂和未开发的用户界面以及所需的大量计算机设置，该技术对临床医生和生物研究人员的可翻译性是有限的。我们开发了一个插件，用于分割整个幻灯片图像（WSIs），具有易于使用的图形用户界面。这个插件运行一个最先进的卷积神经网络，用于在云中分割WSI。我们的插件是建立在开源工具HistomicsTK由Kitware公司。（克利夫顿公园，纽约），其提供WSI数据集的远程数据管理和查看能力。通过互联网访问这个工具的能力将促进计算非专家的广泛使用。用户可以轻松地将幻灯片上传到安装了我们插件的服务器，并远程执行分割分析。这个插件是开源的，一旦经过训练，就能够应用于任何病理结构的分割。为了验证概念，我们已经训练它从肾组织图像中分割肾小球，并在保持组织切片上演示。
Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. This plugin runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform the segmentation analysis remotely. This plugin is open source and once trained, has the ability to be applied to the segmentation of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, demonstrating it on holdout tissue slides.