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
One of the challenges in working in digital pathology is that the associated images can be excessively large, too large to load fully into memory, as well as too large to use in common pipelines. For example, a Aperio SVS file that we’ll look at today is 60,000 x 42,600 pixels. If we tried to load such an image, in RGB space, uncompressed it would require ~7GB, making it too large to consider using in our deep learning pipelines as there wouldn’t be enough RAM on the GPU for both the data and the filter activations.
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: