5
$\begingroup$

I'm working on my last year project where I'm given digitized WSI (Whole Slide Images), though they're fairly small around 1390x1040 size (which is unusual). These images are of cases of Glioblastoma Multiforme (brain cancer) which is stained with Ki-64 index, which results in what I assume malignant parts being marked as brown. Here's a small example of what I'm looking at. snippet of one of the images

My objective in simple terms is to count the blue and brown cells (estimation of proliferation indices) which based on what I've researched is a segmentation problem. I've also came to conclusion that AlexNet is a successful architecture for this purpose. However, the trouble I'm having is that this data is unlabeled and as a Comp Sci student, I don't think I have enough expertise to annotate the ground truth values. My question the boils down to this, are there any alternative methods should I explore, or should I drop this dataset altogether?

$\endgroup$
  • $\begingroup$ You could try some other approach perhaps? Clustering + analyzing the results. If you are lucky those images with brain cancer could be clustered together. $\endgroup$ – Carl Rynegardh Mar 14 '19 at 9:08
0
$\begingroup$

AlexNet (and also VGG, RestNET and other RNNs) are supervised learning approaches, so you need a labelled dataset to proceed.

In your case looks like you do not have the labels, so I do not think you can make use of such approaches. You might want to look into unsupervised learning techniques instead.

The other option is, of course, to find a labelled dataset which given your task, I would recommend.

$\endgroup$
0
$\begingroup$

Potentially what you could do is use a GMM with 3 modes to cluster the image into 3 "partitions". The first would contain all the pixels with blue, the second all the pixels with brown, and the final should contain the background pixels. You would cluster based on the raw RGB values (unless you could calculate some more semantic deep learning based feature for each pixel to cluster). You would then do some form of colour sampling on the pixels assigned to each of the 3 modes to determine which are the brown blobs and which are the blue blobs. You can count easily by computing the binary image from the GMM blobs.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.