A no-reference quality metric for retinal vessel tree segmentation

Authors: Galdran, A., Costa, P., Bria, A., Araújo, T., Mendonça, A.M. and Campilho, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11070 LNCS

Pages: 82-90

eISSN: 1611-3349

ISBN: 9783030009274

ISSN: 0302-9743

DOI: 10.1007/978-3-030-00928-1_10

Abstract:

Due to inevitable differences between the data used for training modern CAD systems and the data encountered when they are deployed in clinical scenarios, the ability to automatically assess the quality of predictions when no expert annotation is available can be critical. In this paper, we propose a new method for quality assessment of retinal vessel tree segmentations in the absence of a reference ground-truth. For this, we artificially degrade expert-annotated vessel map segmentations and then train a CNN to predict the similarity between the degraded images and their corresponding ground-truths. This similarity can be interpreted as a proxy to the quality of a segmentation. The proposed model can produce a visually meaningful quality score, effectively predicting the quality of a vessel tree segmentation in the absence of a manually segmented reference. We further demonstrate the usefulness of our approach by applying it to automatically find a threshold for soft probabilistic segmentations on a per-image basis. For an independent state-of-the-art unsupervised vessel segmentation technique, the thresholds selected by our approach lead to statistically significant improvements in F1-score (+2.67%) and Matthews Correlation Coefficient (+ 3.11%) over the thresholds derived from ROC analysis on the training set. The score is also shown to correlate strongly with F1 and MCC when a reference is available.

Source: Scopus

A No-Reference Quality Metric for Retinal Vessel Tree Segmentation

Authors: Galdran, A., Costa, P., Bria, A., Araujo, T., Mendonca, A.M. and Campilho, A.

Journal: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I

Volume: 11070

Pages: 82-90

eISSN: 1611-3349

ISBN: 978-3-030-00927-4

ISSN: 0302-9743

DOI: 10.1007/978-3-030-00928-1_10

Source: Web of Science (Lite)

A No-Reference Quality Metric for Retinal Vessel Tree Segmentation.

Authors: Galdran, A., Costa, P., Bria, A., Araújo, T., Mendonça, A.M. and Campilho, A.

Editors: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fichtinger, G.

Journal: MICCAI (1)

Volume: 11070

Pages: 82-90

Publisher: Springer

ISBN: 978-3-030-00927-4

https://doi.org/10.1007/978-3-030-00928-1

Source: DBLP