  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
Lirot. ai, a novel platform for facilitating and crowd-sourcing image
segmentations. Methods: Lirot. ai is composed of three components; an iPadOS
client application named Lirot. ai-app, a backend server named Lirot. ai-server
and a python API name Lirot. ai-API. Lirot. ai-app was developed in Swift 5.6
and Lirot. ai-server is a firebase backend. Lirot. ai-API allows the management
of the database. Lirot. ai-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
Lirot. ai 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.
