A No-Reference Quality Metric for Retinal Vessel Tree Segmentation

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Adrian Galdran (Author) Other; Pedro Costa (Author) Other; Alessandro Bria (Author) Other; Teresa Araújo (Author) Other; Ana Maria Mendonça (Author) FEUP; Aurélio Campilho (Author) FEUP

Conference proceedings International

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

Pages: 82-90

21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Granada, SPAIN, SEP 16-20, 2018

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00P-KDD

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

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9