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arxiv: 2405.18383 · v3 · pith:GMTFRRJ3new · submitted 2024-05-28 · 💻 cs.CV · cs.AI· cs.HC· cs.LG

Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge

Dominic LaBella , Valeriia Abramova , Mehdi Astaraki , Andre Ferreira , Zhifan Jiang , Mason C. Cleveland , Ramandeep Kang , Uma M. Lal-Trehan Estrada
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Cansu Yalcin Rachika E. Hamadache Clara Lisazo Adri\`a Casamitjana Joaquim Salvi Arnau Oliver Xavier Llad\'o Iuliana Toma-Dasu Tiago Jesus Behrus Puladi Jens Kleesiek Victor Alves Jan Egger Daniel Capell\'an-Mart\'in Abhijeet Parida Austin Tapp Xinyang Liu Maria J. Ledesma-Carbayo Jay B. Patel Thomas N. McNeal Maya Viera Owen McCall Albert E. Kim Elizabeth R. Gerstner Christopher P. Bridge Katherine Schumacher Michael Mix Kevin Leu Shan McBurney-Lin Pierre Nedelec Javier Villanueva-Meyer David R. Raleigh Jonathan Shapey Tom Vercauteren Kazumi Chia Marina Ivory Theodore Barfoot Omar Al-Salihi Justin Leu Lia M. Halasz Yuri S. Velichko Chunhao Wang John P. Kirkpatrick Scott R. Floyd Zachary J. Reitman Trey C. Mullikin Eugene J. Vaios Christina Huang Ulas Bagci Sean Sachdev Jona A. Hattangadi-Gluth Tyler M. Seibert Nikdokht Farid Connor Puett Matthew W. Pease Kevin Shiue Syed Muhammad Anwar Shahriar Faghani Peter Taylor Pranav Warman Jake Albrecht Andr\'as Jakab Mana Moassefi Verena Chung Rong Chai Alejandro Aristizabal Alexandros Karargyris Hasan Kassem Sarthak Pati Micah Sheller Nazanin Maleki Rachit Saluja Florian Kofler Christopher G. Schwarz Philipp Lohmann Phillipp Vollmuth Louis Gagnon Maruf Adewole Hongwei Bran Li Anahita Fathi Kazerooni Nourel Hoda Tahon Udunna Anazodo Ahmed W. Moawad Bjoern Menze Marius George Linguraru Mariam Aboian Benedikt Wiestler Ujjwal Baid Gian-Marco Conte Andreas M. Rauschecker Ayman Nada Aly H. Abayazeed Raymond Huang Maria Correia de Verdier Jeffrey D. Rudie Spyridon Bakas Evan Calabrese
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classification 💻 cs.CV cs.AIcs.HCcs.LG
keywords radiotherapyplanningsegmentationautomatedbrats-men-rtchallengemeningiomatarget
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The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case included a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk post-operative site. Target volume annotations adhered to established radiotherapy planning protocols, ensuring consistency across cases and institutions, and were approved by expert neuroradiologists and radiation oncologists. Six participating teams developed, containerized, and evaluated automated segmentation models using this comprehensive dataset. Team rankings were assessed using a modified lesion-wise Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95HD). The best reported average lesion-wise DSC and 95HD was 0.815 and 26.92 mm, respectively. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. We describe the design and results from the BraTS-MEN-RT challenge.

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