1,473 research outputs found
Segmentation of Tubular Structures Using Iterative Training with Tailored Samples
We propose a minimal path method to simultaneously compute segmentation masks
and extract centerlines of tubular structures with line-topology. Minimal path
methods are commonly used for the segmentation of tubular structures in a wide
variety of applications. Recent methods use features extracted by CNNs, and
often outperform methods using hand-tuned features. However, for CNN-based
methods, the samples used for training may be generated inappropriately, so
that they can be very different from samples encountered during inference. We
approach this discrepancy by introducing a novel iterative training scheme,
which enables generating better training samples specifically tailored for the
minimal path methods without changing existing annotations. In our method,
segmentation masks and centerlines are not determined after one another by
post-processing, but obtained using the same steps. Our method requires only
very few annotated training images. Comparison with seven previous approaches
on three public datasets, including satellite images and medical images, shows
that our method achieves state-of-the-art results both for segmentation masks
and centerlines.Comment: Accepted to IEEE/CVF International Conference on Computer Vision
(ICCV), Paris, 202
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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