  The lack of annotated datasets is a major bottleneck for training new
task-specific supervised machine learning models, considering that manual
annotation is extremely expensive and time-consuming. To address this problem,
we present MONAI Label, a free and open-source framework that facilitates the
development of applications based on artificial intelligence (AI) models that
aim at reducing the time required to annotate radiology datasets. Through MONAI
Label, researchers can develop AI annotation applications focusing on their
domain of expertise. It allows researchers to readily deploy their apps as
services, which can be made available to clinicians via their preferred user
interface. Currently, MONAI Label readily supports locally installed (3D
Slicer) and web-based (OHIF) frontends and offers two active learning
strategies to facilitate and speed up the training of segmentation algorithms.
MONAI Label allows researchers to make incremental improvements to their
AI-based annotation application by making them available to other researchers
and clinicians alike. Additionally, MONAI Label provides sample AI-based
interactive and non-interactive labeling applications, that can be used
directly off the shelf, as plug-and-play to any given dataset. Significant
reduced annotation times using the interactive model can be observed on two
public datasets.
