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.
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