BACKGROUND: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners. In this paper, we propose an open-source framework to provide AI-enabled medical image analysis through the network. METHODS: TOMAAT provides a cloud environment for general medical image analysis, composed of three basic components: (i) an announcement service, maintaining a public registry of (ii) multiple distributed server nodes offering various medical image analysis solutions, and (iii) client software offering simple interfaces for users. Deployment is realized through HTTP-based communication, along with an API and wrappers for common image manipulations during pre- and post-processing. RESULTS: We demonstrate the utility and versatility of TOMAAT on several hallmark medical image analysis tasks: segmentation, diffeomorphic deformable atlas registration, landmark localization, and workflow integration. Through TOMAAT, the high hardware demands, setup and model complexity of demonstrated approaches are transparent to users, who are provided with simple client interfaces. We present example clients in three-dimensional Slicer, in the web browser, on iOS devices and in a commercially available, certified medical image analysis suite. CONCLUSION: TOMAAT enables deployment of state-of-the-art image segmentation in the cloud, fostering interaction among deep learning researchers and medical collaborators in the clinic. Currently, a public announcement service is hosted by the authors, and several ready-to-use services are registered and enlisted at http://tomaat.cloud.
BACKGROUND: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners. In this paper, we propose an open-source framework to provide AI-enabled medical image analysis through the network. METHODS: TOMAAT provides a cloud environment for general medical image analysis, composed of three basic components: (i) an announcement service, maintaining a public registry of (ii) multiple distributed server nodes offering various medical image analysis solutions, and (iii) client software offering simple interfaces for users. Deployment is realized through HTTP-based communication, along with an API and wrappers for common image manipulations during pre- and post-processing. RESULTS: We demonstrate the utility and versatility of TOMAAT on several hallmark medical image analysis tasks: segmentation, diffeomorphic deformable atlas registration, landmark localization, and workflow integration. Through TOMAAT, the high hardware demands, setup and model complexity of demonstrated approaches are transparent to users, who are provided with simple client interfaces. We present example clients in three-dimensional Slicer, in the web browser, on iOS devices and in a commercially available, certified medical image analysis suite. CONCLUSION: TOMAAT enables deployment of state-of-the-art image segmentation in the cloud, fostering interaction among deep learning researchers and medical collaborators in the clinic. Currently, a public announcement service is hosted by the authors, and several ready-to-use services are registered and enlisted at http://tomaat.cloud.