Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
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2024
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| author | LaBella, Dominic Abramova, Valeriia Astaraki, Mehdi Ferreira, Andre Jiang, Zhifan Cleveland, Mason C. Kang, Ramandeep Estrada, Uma M. Lal-Trehan Yalcin, Cansu Hamadache, Rachika E. Lisazo, Clara Casamitjana, Adrià Salvi, Joaquim Oliver, Arnau Lladó, Xavier Toma-Dasu, Iuliana Jesus, Tiago Puladi, Behrus Kleesiek, Jens Alves, Victor Egger, Jan Capellán-Martín, Daniel Parida, Abhijeet Tapp, Austin Liu, Xinyang Ledesma-Carbayo, Maria J. Patel, Jay B. McNeal, Thomas N. Viera, Maya McCall, Owen Kim, Albert E. Gerstner, Elizabeth R. Bridge, Christopher P. Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Raleigh, David R. Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia M. Velichko, Yuri S. Wang, Chunhao Kirkpatrick, John P. Floyd, Scott R. Reitman, Zachary J. Mullikin, Trey C. Vaios, Eugene J. Huang, Christina Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Chai, Rong Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Maleki, Nazanin Saluja, Rachit Kofler, Florian Schwarz, Christopher G. Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan |
| author_facet | LaBella, Dominic Abramova, Valeriia Astaraki, Mehdi Ferreira, Andre Jiang, Zhifan Cleveland, Mason C. Kang, Ramandeep Estrada, Uma M. Lal-Trehan Yalcin, Cansu Hamadache, Rachika E. Lisazo, Clara Casamitjana, Adrià Salvi, Joaquim Oliver, Arnau Lladó, Xavier Toma-Dasu, Iuliana Jesus, Tiago Puladi, Behrus Kleesiek, Jens Alves, Victor Egger, Jan Capellán-Martín, Daniel Parida, Abhijeet Tapp, Austin Liu, Xinyang Ledesma-Carbayo, Maria J. Patel, Jay B. McNeal, Thomas N. Viera, Maya McCall, Owen Kim, Albert E. Gerstner, Elizabeth R. Bridge, Christopher P. Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Raleigh, David R. Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia M. Velichko, Yuri S. Wang, Chunhao Kirkpatrick, John P. Floyd, Scott R. Reitman, Zachary J. Mullikin, Trey C. Vaios, Eugene J. Huang, Christina Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Chai, Rong Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Maleki, Nazanin Saluja, Rachit Kofler, Florian Schwarz, Christopher G. Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge LaBella, Dominic Abramova, Valeriia Astaraki, Mehdi Ferreira, Andre Jiang, Zhifan Cleveland, Mason C. Kang, Ramandeep Estrada, Uma M. Lal-Trehan Yalcin, Cansu Hamadache, Rachika E. Lisazo, Clara Casamitjana, Adrià Salvi, Joaquim Oliver, Arnau Lladó, Xavier Toma-Dasu, Iuliana Jesus, Tiago Puladi, Behrus Kleesiek, Jens Alves, Victor Egger, Jan Capellán-Martín, Daniel Parida, Abhijeet Tapp, Austin Liu, Xinyang Ledesma-Carbayo, Maria J. Patel, Jay B. McNeal, Thomas N. Viera, Maya McCall, Owen Kim, Albert E. Gerstner, Elizabeth R. Bridge, Christopher P. Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Raleigh, David R. Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia M. Velichko, Yuri S. Wang, Chunhao Kirkpatrick, John P. Floyd, Scott R. Reitman, Zachary J. Mullikin, Trey C. Vaios, Eugene J. Huang, Christina Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Chai, Rong Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Maleki, Nazanin Saluja, Rachit Kofler, Florian Schwarz, Christopher G. Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2405.18383 |