English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Blood vessel segmentation is one of the most studied topics in computervision, due to its relevance in daily clinical practice. Despite the evolutionthe field has been facing, especially after the dawn of deep learning,important challenges are still not solved. One of them concerns the consistencyof the topological properties of the vascular trees, given that the bestperforming methodologies do not directly penalize mistakes such as brokensegments and end up producing predictions with disconnected trees. This isparticularly relevant in graph-like structures, such as blood vessel trees,given that it puts at risk the characterization steps that follow thesegmentation task. In this paper, we propose a similarity index which capturesthe topological consistency of the predicted segmentations having as referencethe ground truth. We also design a novel loss function based on themorphological closing operator and show how it allows to learn deep neuralnetwork models which produce more topologically coherent masks. Our experimentstarget well known retinal benchmarks and a coronary angiogram database.

Topological Similarity Index and Loss Function for Blood Vessel Segmentation