PARROT - A Versatile Platform for AI-Driven Image Segmentation and Dose Prediction

Sadre, Wei;Huet-Dastarac, Margerie;Deffet, Sylvain;Sterpin, Edmond;Lee, John;et.al.
(2024) 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024) — Location: Manchester, UK (19.November.2024)

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Abstract
This paper details the design and architecture of PARROT (Platform for ARtificial intelligence guided Radiation Oncology Treatment), an open-source, Web-based platform that aims to improve therapeutic decision-making through advanced artificial intelligence (AI) techniques. The primary goals of PARROT are to facilitate the visualization of the outputs of AI models, to provide tools for annotating and modifying AI segmentation results, and to assist in comparing treatment plans and selecting the most suitable option. PARROT addresses common challenges in AI research, such as simplifying the execution and management of AI experiments and facilitating the comparison of results between published and institution-trained models. It enables users to interact with AI models for radiation oncology in real time, allowing them to understand and refine model outputs by visually inspecting and correcting biases. PARROT provides an end-to-end workflow with an easy-to-use graphical interface that promotes the implementation of AI models in clinical settings.
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Sadre, W., Huet-Dastarac, M., Deffet, S., Sterpin, E., Barragan Montero, A. M., Jodogne, S., & Lee, J. (2024). PARROT - A Versatile Platform for AI-Driven Image Segmentation and Dose Prediction. Lecture Notes in Electrical Engineering, 1372, 474-483. https://doi.org/10.1007/978-981-96-3863-5_43 (Original work published 2024)