OCT Image Synthesis through Deep Generative Models

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Melo, T (Author) Other; Jaime S Cardoso (Author) FEUP; Ângela Carneiro (Author) FMUP; Aurélio Campilho (Author) FEUP; Ana Maria Mendonça (Author) FEUP

Conference proceedings International

Title: Proceedings - IEEE Symposium on Computer-Based Medical Systems

Pages: 561-566

36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023

L¿Aquila, 22 June 2023 through 24 June 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00Y-V72

DOI: 10.1109/cbms58004.2023.00279

Abstract (EN): The development of accurate methods for OCT image analysis is highly dependent on the availability of large annotated datasets. As such datasets are usually expensive and hard to obtain, novel approaches based on deep generative models have been proposed for data augmentation. In this work, a flow-based network (SRFlow) and a generative adversarial network (ESRGAN) are used for synthesizing high-resolution OCT B-scans from low-resolution versions of real OCT images. The quality of the images generated by the two models is assessed using two standard fidelity-oriented metrics and a learned perceptual quality metric. The performance of two classification models trained on real and synthetic images is also evaluated. The obtained results show that the images generated by SRFlow preserve higher fidelity to the ground truth, while the outputs of ESRGAN present, on average, better perceptual quality. Independently of the architecture of the network chosen to classify the OCT B-scans, the model's performance always improves when images generated by SRFlow are included in the training set.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6