Installing Caffe on the Ohio Super Computing (OSC) Ruby Cluster

One of the perks of working at Case Western Reserve is that we often qualify for access to cutting edge resource and special projects. In this case, since our digital histology deep learning work requires a large number of GPUs to analyze thousands of patients, we were granted access to the OSC Ruby cluster, which has 20 NVIDIA Tesla K40 GPUs. Since the cluster has only recently been setup, there was some leg work required on our end to get Caffe  fully up and running, without root access, which we’ll document here.

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Exporting Bisque Gobjects/Annotations as Binary Masks into Matlab

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.

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Working with Aperio SVS files in Matlab – Converting Annotations to Binary Masks

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.
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Working with Aperio SVS files in Matlab – Loading Sub-Sections

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).
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