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The Image BiomarkerStandardizationInitiative:StandardizedQuantitativeRadiomics forHigh-ThroughputImage-basedPhenotyping Alex ZwanenburgMartin VallièresMahmoud A. Abdalah2020 год

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
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Аннотация: The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.
Год издания: 2020
Авторы: Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J.W.L. Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Irène Buvat, Gary Cook, Christos Davatzikos, Adrien Depeursinge, Marie-Charlotte Desseroit, N. Dinapoli, Cuong V. Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Fedorov, Roberto Gatta, Robert J. Gillies, Vicky Goh, Michael Götz, Matthias Gückenberger, Sung Min Ha, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T. H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus Maier‐Hein, Olivier Morin, Henning Müller, Sandy Napel, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Elisabeth Pfaehler, Arman Rahmim, Arvind U K Rao, Jonas Scherer, Muhammad Musib Siddique, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J.H.M. Steenbakkers, Stephanie Tanadini‐Lang, Daniela Thorwarth, Esther G.C. Troost, Taman Upadhaya, Vincenzo Valentini, Lisanne V. van Dijk, Joost J. M. van Griethuysen, Floris H. P. van Velden, P. Whybra, Christian Richter, Steffen Löck
Издательство: Radiological Society of North America
Источник: Radiology
Ключевые слова: Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, Colorectal Cancer Surgical Treatments
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