Using Deep Learning for Segmentation and Counting within Microscopy Data Using Deep Learning for Segmentation and Counting within Microscopy Data
Paper summary Given microscopy cell data, this work tries to determine the number of cells in the image. The whole pipeline is composed of two steps: 1. Cells segmentation: - Feature Pyramid Network is used for generating a foreground mask; - The last output of FPN is used for predicting mean foreground masks and aleatoric uncertainty masks. Each mask in both outputs is trained with aleatoric loss $ \frac{||y_{pred} - y_{gt} ||^2}{2\sigma} + \log{2\sigma}$ and [total-variational](https://en.wikipedia.org/wiki/Total_variation_denoising) loss. https://i.imgur.com/ssTuGVe.png 2. Cell counting: - VGG-11 network is used as a feature extractor from the predicted foreground segmentation masks. There are two output branches following VGG: cell count branch and estimated variance branch. Training is done using L2 loss function with aleatoric uncertainty for cell counts. https://i.imgur.com/aijZn7e.png While the idea to utilize neural networks to count cells in the image seems fascinating, the real benefit of such system in production is quite questionable. Specifically, why would you need to add a VGG-like feature extractor on top of already predicted cell segmentation masks, if you could simply do more work in segmentation network (i.e. separate cells better, predict objectness/contour) and get the number of cells directly from the predicted masks?
arxiv.org
scholar.google.com
Using Deep Learning for Segmentation and Counting within Microscopy Data
Hernández, Carlos X. and Sultan, Mohammad M. and Pande, Vijay S.
arXiv e-Print archive - 2018 via Local Bibsonomy
Keywords: dblp


Summary by Oleksandr Bailo 6 months ago
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