9,037 research outputs found
Introducing Geometry in Active Learning for Image Segmentation
We propose an Active Learning approach to training a segmentation classifier
that exploits geometric priors to streamline the annotation process in 3D image
volumes. To this end, we use these priors not only to select voxels most in
need of annotation but to guarantee that they lie on 2D planar patch, which
makes it much easier to annotate than if they were randomly distributed in the
volume. A simplified version of this approach is effective in natural 2D
images. We evaluated our approach on Electron Microscopy and Magnetic Resonance
image volumes, as well as on natural images. Comparing our approach against
several accepted baselines demonstrates a marked performance increase
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
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