MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866909216088784896 |
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| author | Diaz-Pinto, Andres Alle, Sachidanand Nath, Vishwesh Tang, Yucheng Ihsani, Alvin Asad, Muhammad Pérez-García, Fernando Mehta, Pritesh Li, Wenqi Flores, Mona Roth, Holger R. Vercauteren, Tom Xu, Daguang Dogra, Prerna Ourselin, Sebastien Feng, Andrew Cardoso, M. Jorge |
| author_facet | Diaz-Pinto, Andres Alle, Sachidanand Nath, Vishwesh Tang, Yucheng Ihsani, Alvin Asad, Muhammad Pérez-García, Fernando Mehta, Pritesh Li, Wenqi Flores, Mona Roth, Holger R. Vercauteren, Tom Xu, Daguang Dogra, Prerna Ourselin, Sebastien Feng, Andrew Cardoso, M. Jorge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_12362 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images Diaz-Pinto, Andres Alle, Sachidanand Nath, Vishwesh Tang, Yucheng Ihsani, Alvin Asad, Muhammad Pérez-García, Fernando Mehta, Pritesh Li, Wenqi Flores, Mona Roth, Holger R. Vercauteren, Tom Xu, Daguang Dogra, Prerna Ourselin, Sebastien Feng, Andrew Cardoso, M. Jorge Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2203.12362 |