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

Abstract

The lack of annotated datasets is a major challenge in training new task-specific supervised AI algorithms as manual annotation is expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source platform that facilitates the development of AI-based applications that aim at reducing the time required to annotate 3D medical image datasets. Through MONAI Label researchers can develop 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 (3DSlicer) 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 labeling apps by making them available to other researchers and clinicians alike. Lastly, MONAI Label provides sample labeling apps, namely DeepEdit and DeepGrow, demonstrating dramatically reduced annotation times.

Deep Learning Enabled Multi-Organ Segmentation of Mouse Embryos

“…The pipeline for development of our trained model for segmentation of fetal mouse scans and its deployment in the opensource, user-friendly MEMOS module is summarized in Figure 1. We implemented the deep leaning network for MEMOS using PyTorch and the Medical Open Network for Artificial Intelligence (MONAI) libraries (Diaz-Pinto, 2022). MONAI is an open-source framework for deep learning customized to work with healthcare imaging, including image input and output functions, data processing and image transformation pipelines.…”

Unsupervised Segmentation of Industrial X-ray Computed Tomography Data with the Segment Anything Model

“…Therefore, methods have been developed to support the domain expert in the segmentation. Diaz et al [6,7] implemented functionalities to support the expert labeling of data for the deep learning framework MONAI (MONAI Label). In Segment Anything, a similar approach was used [5].…”

Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets

“…We suggest further approaches to the introduction of annotations by considering different people with variable levels of expertise, allowing non-experts to carry out the main tasks at their level of skill under the supervision of expert pathologists. Tools like MONAI [68,69] and Quick Annotator [58] are available to make this manual adjustment and thereby facilitate better automatic annotation proposals. These tools, which use an active learning framework for continuous learning, can be integrated into digital pathology and WSI analysis platforms like QuPath [70].…”