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.Continue reading The noise in our digital pathology slides
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 any image and convert it into an OpenSlide compatible WSI, with embedded metadata.Continue reading Converting an existing image into an Openslide compatible format
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.Continue reading Exporting and re-importing annotations from QuPath for usage in machine learning
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:Continue reading Computationally creating a PowerPoint presentation of experimental results using Python
Thanks to everyone in Bern for their attendance at our workshop!
Helping to introduce these concepts to our clinical collaborators is incredibly important for advancing our field, so if you’re interested in hosting a workshop, please feel free to reach out!
Animation below speaks for itself : )
Finally put together a script which makes jupyter notebooks plots interactive, such that when hovering over a scatter point plot, the underlying image displays, see demo + code below:
Code is available here: https://github.com/choosehappy/Snippets/blob/master/interactive_image_popup_on_hover.py
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.
Thanks to everyone for their attendance in our full-day training course in Basel, Switzerland!
Deep learning (DL) models have been performing exceptionally well on a number of challenging tasks lately. Unfortunately, given the current blackbox nature of these DL models, it is difficult to try and “understand” what the network is seeing and how it is making its decisions. Building upon our previous post discussing how to train a DenseNet for classification, we discuss here how to apply various visualization techniques to enable us to interrogate the network. The code here is designed as drop-in functionality for any network trained using the previous post, hopefully easing the burden of its implementation.