Abstract #3777
Lifelong collaborative learning improves the performance of complex muscle MR image segmentation tasks
Francesco Santini1,2, Jakob Wasserthal2, Abramo Agosti3, Xeni Deligianni1,2, Kevin R Keene4, Hermien E Kan5, Stefan Sommer6,7,8, Christoph Stuprich9, Fengdan Wang10, Claudia Weidensteiner1,11, Giulia Manco12, Valentina Mazzoli13, Arjun Desai14, and Anna Pichiecchio12,15
1Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 2Research Coordination Team, Department of Radiology, University Hospital Basel, Basel, Switzerland, 3Department of Mathematics, University of Pavia, Pavia, Italy, 4Department of Neurology, Leiden University Medical Center, Leiden, Netherlands, 5C.J. Gorter MRI Centre, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, 6Siemens Healthineers International AG, Zurich, Switzerland, 7Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland, 8Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Lausanne, Switzerland, 9University Hospital Erlangen, Erlangen, Germany, 10Peking Union Medical College, Beijing, China, 11Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, 12Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy, 13Department of Radiology, Stanford University, Stanford, CA, United States, 14Departments of Electrical Engineering & Radiology, Stanford University, Stanford, CA, United States, 15Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
Synopsis
Keywords: Software Tools, Machine Learning/Artificial IntelligenceAn open-source, federated-learning-based segmentation software termed Dafne (Deep Anatomical Federated Network) is presented. This software continuously adapts the deep learning models used for the segmentation (currently for the muscles of the leg and thigh) based on the input of the users, who are in multiple institutions. This software was validated through data usage statistics of more than 50 users and through a retrospective study on 38 datasets of patients with suspected myositis, showing that the continuous learning approach is able to improve and generalize the performance of the original models.
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