{"ID":2868282,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17281","arxiv_id":"2509.17281","title":"Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform","abstract":"High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology \u0026 artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challenge 2025, a landmark initiative to develop accurate brain tumor segmentation algorithms. Fifty-six medical students \u0026 radiology trainees volunteered to annotate brain tumor MR images for the BraTS challenges of 2023 \u0026 2024, guided by faculty-led didactics on neuropathology MRI. Among the 56 annotators, 14 select volunteers were then paired with neuroradiology faculty for guided one-on-one annotation sessions for BraTS 2025. Lectures on neuroanatomy, pathology \u0026 AI, journal clubs \u0026 data scientist-led workshops were organized online. Annotators \u0026 audience members completed surveys on their perceived knowledge before \u0026 after annotations \u0026 lectures respectively. Fourteen coordinators, each paired with a neuroradiologist, completed the data annotation process, averaging 1322.9+/-760.7 hours per dataset per pair and 1200 segmentations in total. On a scale of 1-10, annotation coordinators reported significant increase in familiarity with image segmentation software pre- and post-annotation, moving from initial average of 6+/-2.9 to final average of 8.9+/-1.1, and significant increase in familiarity with brain tumor features pre- and post-annotation, moving from initial average of 6.2+/-2.4 to final average of 8.1+/-1.2. We demonstrate an innovative offering for providing neuroradiology \u0026 AI education through an image segmentation challenge to enhance understanding of algorithm development, reinforce the concept of data reference standard, and diversify opportunities for AI-driven image analysis among future physicians.","short_abstract":"High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology \u0026 artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challen...","url_abs":"https://arxiv.org/abs/2509.17281","url_pdf":"https://arxiv.org/pdf/2509.17281v1","authors":"[\"Raisa Amiruddin\",\"Nikolay Y. Yordanov\",\"Nazanin Maleki\",\"Pascal Fehringer\",\"Athanasios Gkampenis\",\"Anastasia Janas\",\"Kiril Krantchev\",\"Ahmed Moawad\",\"Fabian Umeh\",\"Salma Abosabie\",\"Sara Abosabie\",\"Albara Alotaibi\",\"Mohamed Ghonim\",\"Mohanad Ghonim\",\"Sedra Abou Ali Mhana\",\"Nathan Page\",\"Marko Jakovljevic\",\"Yasaman Sharifi\",\"Prisha Bhatia\",\"Amirreza Manteghinejad\",\"Melisa Guelen\",\"Michael Veronesi\",\"Virginia Hill\",\"Tiffany So\",\"Mark Krycia\",\"Bojan Petrovic\",\"Fatima Memon\",\"Justin Cramer\",\"Elizabeth Schrickel\",\"Vilma Kosovic\",\"Lorenna Vidal\",\"Gerard Thompson\",\"Ichiro Ikuta\",\"Basimah Albalooshy\",\"Ali Nabavizadeh\",\"Nourel Hoda Tahon\",\"Karuna Shekdar\",\"Aashim Bhatia\",\"Claudia Kirsch\",\"Gennaro D'Anna\",\"Philipp Lohmann\",\"Amal Saleh Nour\",\"Andriy Myronenko\",\"Adam Goldman-Yassen\",\"Janet R. Reid\",\"Sanjay Aneja\",\"Spyridon Bakas\",\"Mariam Aboian\"]","published":"2025-09-21T23:39:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CY\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}