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

arXiv: https://arxiv.org/abs/2203.12362 · HTML: https://mlsanity.com/paper/2203.12362/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning](https://mlsanity.com/paper/2104.12166/) (2021)
- [Going to Extremes: Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2009.11988/) (2020)
- [Interactive Full Image Segmentation by Considering All Regions Jointly](https://mlsanity.com/paper/1812.01888/) (2018)
- [Interactive segmentation of medical images through fully convolutional neural networks](https://mlsanity.com/paper/1903.08205/) (2019)
- [Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection](https://mlsanity.com/paper/1809.02882/) (2018)

## Built on by (most cited first, 30 of 39)

- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [PanTS: The Pancreatic Tumor Segmentation Dataset](https://mlsanity.com/paper/2507.01291/) (2025)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2308.07003/) (2023)
- [Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues](https://mlsanity.com/paper/2301.09914/) (2023)
- [MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT](https://mlsanity.com/paper/2405.06463/) (2024)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)
- [MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows](https://mlsanity.com/paper/2603.24649/) (2026)
- [Synthetic Data for Robust Stroke Segmentation](https://mlsanity.com/paper/2404.01946/) (2024)
- [RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology](https://mlsanity.com/paper/2605.10761/) (2026)
- [Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation](https://mlsanity.com/paper/2303.13696/) (2023)
- [Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images](https://mlsanity.com/paper/2311.14482/) (2023)
- [Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO](https://mlsanity.com/paper/2406.19057/) (2024)
- [Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations](https://mlsanity.com/paper/2403.15218/) (2024)
- [Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods](https://mlsanity.com/paper/2404.01816/) (2024)
- [Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans](https://mlsanity.com/paper/2405.03713/) (2024)
- [Conformal Risk Control for Pulmonary Nodule Detection](https://mlsanity.com/paper/2412.20167/) (2024)
- [JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images](https://mlsanity.com/paper/2304.07744/) (2023)
- [Segment Any Medical Model Extended](https://mlsanity.com/paper/2403.18114/) (2024)
- [Towards MR-Based Trochleoplasty Planning](https://mlsanity.com/paper/2508.06076/) (2025)
- [Revisiting Data Scaling in Medical Image Segmentation via Topology-Aware Augmentation](https://mlsanity.com/paper/2511.13883/) (2025)
- [Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging](https://mlsanity.com/paper/2601.13677/) (2026)
- [X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging](https://mlsanity.com/paper/2602.09284/) (2026)
- [A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography](https://mlsanity.com/paper/2602.21935/) (2026)
- [Expert-like Bone Ultrasound Segmentation through Expert-in-the-loop Mask-conditioned Progressive Learning](https://mlsanity.com/paper/2609.00473/) (2026)
- [Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning](https://mlsanity.com/paper/2402.07746/) (2024)
- [Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection](https://mlsanity.com/paper/2405.07369/) (2024)
- [deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks](https://mlsanity.com/paper/2408.10656/) (2024)
- [SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation](https://mlsanity.com/paper/2411.12602/) (2024)

## Abstract (arXiv)

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.