CODEWITHSUNDEEP
image-processingdeep-learningimage-segmentationsemantic-segmentationmedical-imaging
Mass17
Mass17

Reputation: 1605

Medical Image Segmentation if the entire ground truth is black

I am new to deep learning. I am working on a CT-scan medical images. I want to use UNet architecture to predict the image segmentation. I have successfully implemented the UNet, however, my prediction is completely black. I think it is because there are images, for which the corresponding ground truth is black (quite a lot of images). So, I suppose it might cause a problem.

If the entire mask is black that implies there are no desired object in the image. An example image is below;

enter image description here

The below is the corresponding ground truth.

enter image description here

I am not sure how to deal with this situation. Should I remove all the (image, ground truth) pairs? CT images are volumetric images. So when my model predict the segmentation in a new test set, it should also detect images with no desired object in it. I would appreciate if someone guide me in this.

dataset: https://www.doc.ic.ac.uk/~rkarim/la_lv_framework/wall/index.html

Upvotes: 4

Views: 1689

Answers (1)

Shai
Shai

Reputation: 114926

Image segmentation is more like pixel classification than image classification.
Therefore, you should not look at the ratio of "blank images"/"object images", but rather the ratio of "blank pixels"/"object pixels". My guess the ratio is much more skewed towards the "blank" pixels.

This means you are dealing with severe class imbalance.

This answer lists focal loss and on-line hard negative mining as good methods for handling class imbalance.

Upvotes: 4

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