Digital whole slide image scanners are designed to take stained tissue on glass slides and digitize them into bytes for usage in the digital world. The process by which slide scanners perform this operation does not produce a perfect digital equivalent of the original slide as the hardware involved (led/blub, camera sensor, quantizer) can introduce some biases during the sampling process. For example, different camera sensors may detect colors with different levels of specificity/accuracy/density, resulting in similar but not perfect representations of the associated real-world subjects.
Concretely, there is often a difference between the color you perceive in the real-world under a microscope versus what you would see if you looked at the corresponding digital copy of the same slide. This blog post discusses how to correct for this discrepancy using ICC profiles.
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.
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 Quick Annotator (QA), a tool which employs a deep learning backend to simultaneously learn and aid the user in the annotation process. A pre-print explaining this tool in more detail is available [here].
In adding new features to HistoQC , I stumbled upon a very interesting insight that I thought I would take a moment to share. The amount of noise and artifacts in digital pathology (DP) whole slide images (WSI) is far more extensive than I had previously thought.
Many digital pathology tools (e.g., our quality control tool, HistoQC), employ Openslide, a library for reading whole slide images (WSI). Openslide provides a reliable abstraction away from a number of proprietary WSI file-formats, such that a single programmatic interface can be employed to access WSI meta and image data.
Unfortunately, when smaller regions of interest, or new images, are created in tif/png/jpg formats they no longer remain compatible with OpenSlide. This blog post discusses how to take anyimage and convert it into an OpenSlide compatible WSI, with embedded metadata.
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.
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.
Our paper is out in: Journal of Clinical Oncology: Clinical Cancer Informatics
Purpose: Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artifacts and batch effects; unintentionally introduced during both routine slide preparation (e.g., staining, tissue folding, etc.) as well as digitization (e.g., blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra/inter-reader variability. There is thus a critical need for a reproducible automated approach of precisely localizing artifacts in order to identify slides which need to be reproduced or regions which should be avoided during computational analysis.