#Dump memory cellprofiler how to

We break down this approach into 5 steps: We find that on our dataset we can obtain f-scores in the range of ~.83, without any post-processing, making this DL approach comparable to sophisticated handcrafted approaches. These differences imply that developing a single hand-crafted approach that works well across all cases is challenging. For example, the area of a breast cancer nucleus can vary by over 200% and have notable differences in texture, morphology, and stain absorption. Breast cancer nuclei are, in my opinion, the most challenging to work with because of their large variances in appearance as compared to other organs.

We have specifically chosen to look at the problem of detecting nuclei within H&E stained estrogen receptor positive (ER+) breast cancer images. The overlap resolution techniques are typically applied as post-processing on segmentation outputs, and thus outside of the scope of this tutorial. A recent review of nuclei segmentation literature shows that detecting these nuclei tends not to be extremely challenging, but accurately finding their borders and/or dividing overlapping nuclei is the current challenge. Nuclei segmentation is an important problem for two critical reasons: (a) there is evidence that the configuration of nuclei is correlated with outcome, and (b) nuclear morphology is a key component in most cancer grading schemes.

#Dump memory cellprofiler how to

For guidance on that you can reference this blog post which describes how to install it in an HPC environment (and can easily be adopted for local linux distributions). This text assumes that Caffe is already installed and running.

Please note that there has been an update to the overall tutorial pipeline, which is discussed in full here. This blog posts explains how to train a deep learning nuclear segmentation classifier in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.