使用Paquo直接与QuPath项目文件交互,以用于数字病理学机器学习

This is an updated version of the previously described workflow on how to load and classify annotations/detections created in QuPath for usage in downstream machine learning workflows. The original post described how to use the Groovy programming language used by QuPath to export annotations/detections as GeoJSON from within QuPath, made use of a Python script to classify them, and lastly used another Groovy script to reimport them. If you are not familiar with QuPath and/or its annotations you should probably read the original post first to provide better context and understanding of the respective workflows, as well as being able to appreciate the more elegant approach taken here. If you are already using the described approach, you should be able to easily modify it to follow this newer approach.

继续阅读 使用Paquo直接与QuPath项目文件交互,以用于数字病理学机器学习 →

教程:用于小管分割的快速注释器

The manual labeling of large numbers of objects is a frequent occurrence when training deep learning classifiers in the digital histopathology domain. Often this can become extremely tedious and potentially even insurmountable.

To aid people in this annotation process we have developed and released 快速注释器 (QA),一种使用深度学习后端同时学习和帮助用户进行注释过程的工具。可提供更详细的打印前说明此工具[在这里].

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将现有图像转换为与Openslide兼容的格式

许多数字病理学工具(例如,我们的质量控制工具, HistorQC),雇用 打开幻灯片,一个用于读取整个幻灯片图像(WSI)的库。 Openslide提供了一种可靠的抽象,远离了许多专有的WSI文件格式,因此可以使用单个编程接口来访问WSI元数据和图像数据。

不幸的是,当以tif/png/jpg格式创建较小的感兴趣区域或新图像时,它们不再与OpenSlide兼容。这篇博文讨论了如何 任何 映像并将其转换为与OpenSlide兼容的WSI,其中包含嵌入式元数据。

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Exporting and re-importing annotations from QuPath for usage in machine learning

Update-Nov 2020: Code has now been placed in github which enables the reading and writing of compressed geojson files at all stages of the process described below. Compression reduces the file size by approximately 93% : )

QuPath is a digital pathology tool that has become especially popular because it is both easy to use to and supports a large number of different whole slide image (WSI) file formats. QuPath is also able to perform a number of relevant analytical functions with a few mouse clicks. Of interest in this blog post is mentioning that the pathologists we tend to work with are either already familiar with QuPath, or find it easier to learn versus other tools. As a result, QuPath has become a goto tool for us for both the creation, and review of, annotations and outputs created by our algorithms.

Here we introduce a robust method using GeoJSON for exporting annotations (or cell objects) from QuPath, importing them into python as shapely objects, operating upon them, and then re-importing a modified version of them back into QuPath for downstream usage or review. As an example use case we will be looking at computationally identifying lymphocytes in WSIs of melanoma metastases using a deep learning classifier.

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Computationally creating a PowerPoint presentation of experimental results using Python

This post is an update of the previous post, which discussed how to create a powerpoint slide desk with results using Matlab. In the last couple of years, we have mostly transitioned to python for our digital pathology image analysis, in particular those tasks which employ deep learning. It thus makes sense to port our tools over as well. In this case, we’ll be looking at building powerpoint slide desks using python.

Let’s look at what we want as our final output:

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