MATLAB PROJECT
Bayesian
Polytrees With Learned Deep Features for Multi-Class Cell Segmentation
Abstract:
The recognition of different cell compartments, the types of cells, and
their interactions is a critical aspect of quantitative cell biology. However,
automating this problem has proven to be non-trivial and requires solving
multi-class image segmentation tasks that are challenging owing to the high
similarity of objects from different classes and irregularly shaped structures.
To alleviate this, graphical models are useful due to their ability to make use
of prior knowledge and model inter-class dependences. Directed acyclic graphs,
such as trees, have been widely used to model top-down statistical dependences
as a prior for improved image segmentation. However, using trees, a few
inter-class constraints can be captured. To overcome this limitation, we
propose polytree graphical models that capture label proximity relations more
naturally compared to tree-based approaches. A novel recursive mechanism based
on two-pass message passing was developed to efficiently calculate closed-form
posteriors of graph nodes on polytrees. The algorithm is evaluated on simulated
data and on two publicly available fluorescence microscopy datasets,
outperforming directed trees and three state-of-the-art convolutional neural
networks, namely, SegNet, DeepLab, and PSPNet. Polytrees are shown to
outperform directed trees in predicting segmentation error by highlighting
areas in the segmented image that do not comply with prior knowledge. This
paves the way to uncertainty measures on the resulting segmentation and guide subsequent
segmentation refinem
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