MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images (2203.12362v2)

Abstract: 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 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.

Insightful Overview of MONAI Label Framework

This essay explores the conceptual and functional attributes of MONAI Label, a comprehensive framework designed to ameliorate the intrinsic challenges associated with the annotation of 3D medical images. The absence of annotated datasets imposes a significant impediment to the advancement of task-specific supervised ML models. MONAI Label addresses this by providing a flexible, open-source platform facilitating the development of AI-driven annotation applications tailored to various domains within medical imaging.

Framework Architecture and Capabilities

MONAI Label is methodologically designed to integrate seamlessly with existing medical imaging workflows. It supports two primary forms of user interfaces—locally with 3D Slicer and through web-based access utilizing OHIF—enabling clinician involvement through their preferred interactive environments. Its architecture bifurcates into interactive and non-interactive annotation strategies. The interactive approach comprises sophisticated methods such as DeepGrow and DeepEdit, as well as energy optimization-based scribbles for enhancing segmentation precision through user input. In contrast, the non-interactive method foregrounds a conventional ML approach leveraging integrated networks from MONAI and PyTorch.

Remarkably, MONAI Label distinguishes itself with active learning strategies that streamline the training process. These strategies assimilate aleatoric and epistemic uncertainty values to prioritize complex samples for early labeling, thereby enhancing the effectiveness of resultant models.

Numerical Results and Practical Implications

The framework's efficacy is evidenced by substantial reductions in annotation time, notably with the DeepGrow model, where time per volume annotation dwindles from initial stages to subsequent, more trained iterations. The statistically significant reductions in annotation time—as much as 10x faster annotation in some cases—affirm the potential of MONAI Label in revolutionizing the task of annotation in clinical settings.

From a practical standpoint, the implications of MONAI Label are twofold. First, it democratizes the ability of clinicians and researchers to develop bespoke AI annotation models. Second, it optimizes the model's learning curve by iteratively refining it against user-annotated data, making it adaptable for diverse medical contexts and datasets.

Theoretical Implications and Future Prospects

The incorporation of active learning paradigms into MONAI Label has theoretical ramifications, underpinning the potential for the co-evolution of AI models with accruing data. This feature lays the groundwork for the continuous improvement of AI models in clinical practice settings; an iterative feedback loop allows the augmentation of model robustness against novel anatomical divergences or imaging subtleties.

Looking towards prospective developments, MONAI Label's modularity suggests promising opportunities for its extension in multimodal imaging and integration with emerging diagnostic technologies. Moreover, ongoing advancements in AI frameworks like MONAI Core could fortify rapid algorithmic innovations and more nuanced user interactions, such as ROI-based annotations.

Conclusion

In summary, MONAI Label emerges as a quintessential framework in the domain of 3D medical image annotation, addressing pivotal challenges in labeled data creation while substantially expediting the annotation lifecycle. Its design not only caters to present-day clinical needs but also paves the path for future innovations in automated and interactive image processing. With an open invitation to the research community for feedback and collaboration, MONAI Label stands poised to remain at the forefront of AI-driven medical imaging advancements.