Abstract
Introduction: For supervised deep learning (DL) tasks, researchers need a large annotated dataset. In medical data science, one of the major limitations to develop DL models is the lack of annotated examples in large quantity. This is most often due to the time and expertise required to annotate. We introduce this http URL, a novel platform for facilitating and crowd-sourcing image segmentations. Methods: this http URL is composed of three components; an iPadOS client application named this http URL-app, a backend server named this http URL-server and a python API name this http URL-API. this http URL-app was developed in Swift 5.6 and this http URL-server is a firebase backend. this http URL-API allows the management of the database. this http URL-app can be installed on as many iPadOS devices as needed so that annotators may be able to perform their segmentation simultaneously and remotely. We incorporate Apple Pencil compatibility, making the segmentation faster, more accurate, and more intuitive for the expert than any other computer-based alternative. Results: We demonstrate the usage of this http URL for the creation of a retinal fundus dataset with reference vasculature segmentations. Discussion and future work: We will use active learning strategies to continue enlarging our retinal fundus dataset by including a more efficient process to select the images to be annotated and distribute them to annotators.
Abstract (translated)
URL
https://arxiv.org/abs/2208.10100
