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The lack of annotated datasets is a major bottleneck for training newtask-specific supervised machine learning models, considering that manualannotation is extremely expensive and time-consuming. To address this problem,we present MONAI Label, a free and open-source framework that facilitates thedevelopment of applications based on artificial intelligence (AI) models thataim at reducing the time required to annotate radiology datasets. Through MONAILabel, researchers can develop AI annotation applications focusing on theirdomain of expertise. It allows researchers to readily deploy their apps asservices, which can be made available to clinicians via their preferred userinterface. Currently, MONAI Label readily supports locally installed (3DSlicer) and web-based (OHIF) frontends and offers two active learningstrategies to facilitate and speed up the training of segmentation algorithms.MONAI Label allows researchers to make incremental improvements to theirAI-based annotation application by making them available to other researchersand clinicians alike. Additionally, MONAI Label provides sample AI-basedinteractive and non-interactive labeling applications, that can be useddirectly off the shelf, as plug-and-play to any given dataset. Significantreduced annotation times using the interactive model can be observed on twopublic datasets.

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images