Annotations which stay solely in Bisque aren’t incredibly useful. One of the main reasons for marking up images is to be able to use those annotations for some other purpose, such as training classifiers, computing metrics and features. In this post, we show how to take the annotations from bisque via REST, and convert them into binary masks in matlab.
Once we have images uploaded to Bisque, we might want to be able to overlay annotations on top of them so that users can interact with them. This post quickly goes through the conversation of a binary mask, in matlab, into an XML which can later be imported into Bisque.
Assuming we have the necessary files on our Bisque server (perhaps uploaded with our script), and we have a set of bisque compliant XML annotations (perhaps generated with our script), we would like to upload them to the Bisque server so that they can be evaluated or modified. That is what this post is about 🙂
In this blog post, we discuss how to quickly upload a dataset to Bisque using python. The next blog post will then talk about how to convert binary masks (made in matlab) to annotations usable by Bisque for validation or modification.
In the previous post we discussed how to export annotations from a Ventana Image Viewer program and create binary masks. Now we explain how to do the opposite and import the mask back into Image Viewer.
Previously we looked at extracting annotations from Aperio Svs files. There are other image formats and annotation tools. Another commonly used tool in digital histology is ImageViewer, which makes it possible to view multi-page BigTiff image files.
Continue reading Extract Annotations From ImageViewer Bigtiff xml into Matlab
There are often times when we want to see the boundaries of an annotation overlaid on an image for easier inspection. Using the ‘AlphaData’ layer in matlab this becomes extremely easy and efficient.
Continue reading Overlaying Binary Masks on Images in Matlab
One of the main purposes of having a digital format is to allow experts (e.g., pathologists) to annotate certain structures in the images. Be it nuclei, epithelium/stroma regions, tumor/non-tumor tissue etc. This is easily done with ImageScope and SVS files, but the trick is importing them into Matlab.
Continue reading Working with Aperio SVS files in Matlab – Converting Annotations to Binary Masks
In the previous tutorial we discussed how to load different levels of the image pyramid for Aperio SVS images in Matlab and how they corresponded.
In this tutorial we will extend upon that in 2 critical ways. First, we’d like to be able to load only small sub-sections from images (easy) and then extend upon that so we can identify regions at a low-magnification that we’d like to load the corresponding high-magnification version (harder).
Continue reading Working with Aperio SVS files in Matlab – Loading Sub-Sections
Aperio scanners generate a semi-proprietary file format called SVS. At its heart, SVS files are really a multi-page tiff file storing a pyramid of smaller tiff files of the original image. We’ll look at those here using a SVS file provided by the TCGA (http://cancergenome.nih.gov/) breast cancer cohort: