Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods

Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods

Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perfo...

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Bibliographic Details
Main Authors: Gorana Gojić, Veljko B. Petrović, Dinu Dragan, Dušan B. Gajić, Dragiša Mišković, Vladislav Džinić, Zorka Grgić, Jelica Pantelić, Ana Oros
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Sensors
Subjects:
fundus image
segmentation
segmentation mask
subjective assessment
convolutional neural networks
clinical viability
Online Access:https://www.mdpi.com/1424-8220/22/23/9101

Internet

https://www.mdpi.com/1424-8220/22/23/9101

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